The magneto-mechanical behaviour of structural steel specimens stressed up to the plastic deformation stage was investigated using a 2nd order gradiometer based on Giant Magneto Resistive (GMR) sensors. The correlation between the gradient of the magnetization and the dislocation density before the crack initiation inside the test material was reported. The capability of the GMR scanning sensor to detect the residual magnetization due to the tensile stress with a non-invasive technique was demonstrated. Since 1842, it is well known that the effect called inverse Joule, Villari or piezo-magnetism consists in the changing of magnetic properties of a metallic material when it is stressed or strained. In particular, the susceptibility and the permeability of the material can change because an anisotropic deformation of the crystal lattice is produced by applying the strain. The materials in which it is possible to observe this effect are mainly 3D metal, alloy, intermetallic compounds and in 4f metals (lanthanide compounds).Depending on the type of strain, the elastic, plastic or cyclic different material parameters can change through, such as, the resultant of the magnetic moment, the magnetic hysteresis loop of the material, and the magnetic flux dispersion with nucleation of fatigue cracks, respectively. In all the above situations the physical properties, for example magnetic hysteresis, Barkhausen noise and coercitivity, could change progressively when the fatigue process evolves from its beginning stage until the material finally fractures. When plastic deformation occurs, the first relevant structural alteration is represented by the increasing dislocation density. During the deformation process, at first the dislocation density surges rapidly with hardness, then it saturates and the hardness reaches a maximum value that remains constant even if further deformation occurs. The dislocations produced into the material by the plastic deformation interact and multiply through the crystal lattice, and as a consequence they interact with the magnetic domain wall influencing the magnetic structure and properties of the ferromagnetic materials. The plastic strains are non-linearly coupled with magnetic properties in the case of steels . The expCurrently, to guarantee the safety of large structures, such as buildings and bridges, it is necessary to detect in advance the damage produced during the life cycle of the metallic materials. In particular, to prevent crack initiation, it is useful to detect the starting stage of deformation when the dislocation density begins to increase. To this aim, the correlation between the strain and crack detection has been taken into account. In other words, several non-destructive methods based on the magneto-mechanical effect are able to successfully detect the mechanical stress ,11,12,13To overcome these limitations, other non-destructive testing methods could be used to detect magneto-mechanical effects in metallic alloys, such as the application of several magnetic sensors i.e., Hall probes, the superconducting quantum interference device (SQUIDs), fluxgate and the giant magneto-resistance (GMR) ,17,18,19In this work, to improve the signal to noise ratio, a GMR second order gradiometer was designed and used to monitor magneto-mechanical behaviour in ferromagnetic specimens under a mechanical load. The aim of this work is to demonstrate that an alternative non-destructive method based on GMR sensors allows the correlation of the magnetization signal of the sample both with its mechanical behaviour and the localization of crack initiation, the latter being due to the imaging of the magnetic field.In this work, a dog-bone shaped sample, 6 mm thick, made of mild steel (Fe360) was analysed. This sample shape gives the possibility of investigating the effect of plastic deformation represented by surface deformations called slip bands. The testing sample was demagnetized in a long solenoid with the aim of cutting down the potential presence of any macroscopic residual magnetization. The testing fixture used to the applied load cycles to the sample is made of commercial brass, a non-magnetic material, which does not produce any detectable magnetic contribution that could modify the magnetic field detected nearby the sensors and the sample see .The fixture was essentially composed of a load frame consisting of two stout columns, stiffened by lateral ribs, and two crossheads having cross-sections gradually enlarged in their central region. In the mid-span sections, two holes with squared shape were made to locate the stems of the forks where the specimen was fastened by means of a couple of bolts. The testing load was generated by tightening one of the nuts screwed on the forks’ ends. The shape of the fork stems’ cross-section and holes in the cross-heads was adopted to avoid that the specimens were subjected to a parasitic twist during nut tightening. More details on the adopted fixture are given in Firstly, the sample was demagnetized and then deformed plastically with a stress cycle ranging from 0 to 20 kN and successively, the loading cycle increased up to 40 kN. On each threaded bar, three electric strain gages were installed to measure and control the load. The signals of these strain sensors were calibrated by a universal servo hydraulic testing machine equipped with a load cell. The tensile stress was applied along the x-direction and measured by the strain gauges glued on both sides of the samples see . A giant magneto resistive (GMR) sensor is based on the giant magneto resistive effect, discovered since 1988 as a large change in electrical resistance (from 10 % to 20 %) that occurs when thin, stacked layers of ferromagnetic and non-magnetic materials are exposed to a magnetic field [A single GMR sensor essentially consists of two magnetic layers, one being magnetically hard while the other is soft, separated by a non-magnetic spacer with a total thickness of approximately 10 nm. The magnetization of the soft magnetic layer (detector) changes its direction under an applied external magnetic field with respect to the fixed magnetization of the hard layer, pinned by an antiferromagnetic layer , and arranged into a plexiglass holder to form a second order electronic gradiometer, as shown in The GMR on-chip sensor is a magnetometer configured as a Wheatstone bridge to provide temperature compensation, and it is characterized by two flux concentrators that provide a directionally sensitive output signal. Since these devices are sensitive only in the direction of the supplied current plane, they record the same output for magnetic fields that have the positive or negative direction with respect to the GMR sensitivity axis. The three GMR-sensors’ magnetometer were vertically aligned with a baseline of 10 mm, and each sensor was biased with a constant supply voltage of 9 V. The 1st GMR sensor that was close to the sample (lift-off of 0.5 mm) was the detector, while the other two GMRs (2nd and 3rd) provided references to subtract and reduce the environmental interference. The 2nd order gradiometer based on GMR sensor improves the ability of the GMR magnetometer to reject the environmental interference . To demonstrate the reproducibility of the GMR measurements, the stability of the sensors’ response and the influence of the environmental magnetic field, mainly due to electric power line at 50 Hz, a preliminary study was carried out. In A calibration of the GMR magnetometer and gradiometer configuration to study the effect due to the sensor intrinsic hysteresis was carried out. Using a motorized scanning micrometric stage, the GMR sensor was moved over the electric wire b from riMoreover, It is noted that the magnetic field measured by the single magnetometer has a ripple larger than the gradiometer. This means that a reduction of the noise in an unshielded environment could be obtained using a second order gradiometer configuration. Moreover, the gradiometer output was affected by a five times reduction of the signal amplitude as confirmed by the values at maximum (x = 0).The graph in Generally, it is difficult to detect crack nucleation before 90% of the fatigue life, even if it occurs earlier in the fatigue life cycle and propagates into the specimens very rapidly up to the catastrophic failure of the material. For this reason, it is necessary to detect in advance the fatigue crack initiation to guarantee the safety of large structures made of metallic alloys. Then, one of the NDE useful techniques used to detect the first and third stages of fatigue is represented by magnetic field sensors, such as GMR sensors, which are able to detect the magnetic field variation produced by the mechanical structural changes. In this work, the second order gradiometer based on GMR on-chip sensors was used to achieve this aim. In Furthermore, the experimental results in Moreover, as mentioned before, it is important to detect the plastic deformation by the time a crack failure occurs. This means that it is useful to localize the nucleation areas into the sample by using the magnetic field variation imaging technique. Then, by scanning the sample surface, it is possible to record a magnetic map of the gradiometer signal that highlights the magnetic dislocation, as shown in The magnetic image was obtained using 2nd order GMR gradiometer scanning on a small area of the test sampling, 10 mm × 40 mm , that appear on the structure surface by means of intrusions. Typically, the slip bands come prior to the initiation of the fatigue cracks after plastic deformation. The magnetic field reported in In this work, a non-destructive evaluation technique to localize fatigue effects in the structural steel sample Fe360 by using the magnetic signal is reported. The results demonstrate the relationship between the microstructural changes induced by the applied strain and the associated magnetic field variations detected nearby the surface of the ferromagnetic specimens.A tensile testing machine, made of non-magnetic material, was realized to stress the specimens. Moreover, an experimental set up was developed to record and process the experimental data. The experimental results, obtained using the 2nd order electronic gradiometer based on GMR on-chip sensors, suggest an unconventional method to localize the effect of the fatigue by means of magnetic signal variation. Finally, the reported technique allows the identification of the tensile concentration due to the magneto-mechanical effect before the crack initiation during the tensile load. This information could be useful for the study of the mechanical properties of the large metallic structures during their life cycle.


This paper is a Part I of a literature review documentation describing the currently available and used techniques that are being explored by material scientists and researchers in the field of materials characterizations and testing for both thermal and environmental barrier coatings . This review contains relevant information regarding the most common coating applications and their impact on the durability and life of both the coatings and the substrate materials. It also includes a description of the methodologies of coating applications and their pros and cons. A discussion of the applicability, failure modes and modeling approaches that are presently available and utilized by active researchers in the field is also included. Part II will illustrate an in-depth assessment of various aspects of the available and developing life prediction models for both TBC and EBC and the influence of intrinsic and extrinsic factors on their thermal and mechanical stability. Thermal barrier coatings (TBCs) were developed about three decades ago to be used by engine manufacturers for improving engine performance and fuel efficiency to allow for higher operating temperatures while reducing emissions and costs, increasing the life-time of an engine and reducing maintenance costs. However, upon exposure to hot gas and harsh environmental conditions, they tend to undergo some types of degradation phenomena  which coThus, evaluating coated components and subcomponents made out of monolithic and composite ceramics under gas turbine engine conditions is highly essential to demonstrate that these materials will perform as expected and required under these aggressive environmental circumstances. The EBC system is made up of multiple layers of coatings in which each layer serves a specific purpose . The comThe TBC is comprised of two layers: a metallic bond coat and a ceramic top coat . The bonAdditionally, a reaction product identified as a thermally grown oxide (TGO) develops and grows at the TBC-ceramic interface due to high temperatures. The thermal expansion coefficients of these constituents of the TBC system are different and as a result, this leads to thermal stresses mismatch due to the existence of the temperature gradient in the structure. This causes stress growth in the TGO section due to the incessant formation of TGO due to the presence of high temperature. The stresses in the TBC are affected by the surface roughness of the bond coat and both the TGO growth and the thermal expansion coefficient (CET) mismatch stresses are compressive on flat bond surfaces .t) is given by Equation (1),c and αsubstrate are coefficients of thermal expansion for the coating and the substrate, Ec is the Young’s modulus of the coating, and νc is the Poisson’s ratio of the coating.The mismatch thermal stress  applications remains attractive as the need for greater engine efficiency in aircraft and or improving engine performance is the desire of all engine makers. They further prefer to be able to operate at higher operating temperatures while reducing emissions and costs, increasing the lifetime of an engine, and reducing maintenance costs . HoweverGenerally, both TBCs and EBCs are coating materials that are being considered for as protective systems for CMCs for components used in aeroengine applications. CMCs are materials that are lighter and are able at a temperature that is 200 °C hotter than that is needed for the metallic materials. A protective silica forms in dry air conditions on the surface, which provides stability at temperatures up to 1300 °C for long-term situations. However, in combustion environments with presence of moisture, the silica layer on surface reacts violently leading to surface recession ,9. ThereCurrently, these systems are deposited by various methods, the most popular of which include: (a) air plasma spray (APS); (b) electron-beam-assisted physical vapor deposition (EB-PVD); (c) sputtering; (d) slurry deposition; (e) plasma spray assisted physical vapor deposition (PS-PVD) and (f) chemical deposition process (CVD). The microstructure, elastic and fracture properties of the coating deposited by the above methods can vary significantly. Among the most successful application to apply, EBC is the APS . With thCurrently, among the preferred method for producing TBCs and EBCs is the plasma-spray process. This process is generally initiated by briefly filling ceramic or metallic powder into and arc plasma at high velocity and at high temperature. As this process proceeds, the powder particles melt as they are pushed towards the substrate and as a result, the molten materials forms over the substrate to build up the coating level desired. Parameters controlling the quality of the coating have to be optimized such as productivity of good coating is expected. Also, careful monitoring of the process is required to guarantee reproducibility.The electron-beam physical vapor deposition (EB-PVD) process is another application used to prepare ceramic barrier coatings. During the application of this process, high-energy beam is applied to heat and vaporize the ceramic. The vapor moves under direct line of sight to substrate and it forms as atom-by-atom layer. If zirconia—yttria are used as a ceramic material, the process of application is applied under low partial pressure of oxygen so the stoichiometry of the zirconia is preserved. Application of EB-PVD to deposit costing leads to an interface between the bond coat and the ceramic. Note in the application, the ceramic has a smooth surface while for plasma-sprayed coatings a ceramic rough surface is required. After deposition of an initial thin region, the ceramic exhibits a columnar growth. After deposition of an initial thin region, the ceramic displays columnar growth. This growth is known to help reducing the elastic modulus along the plane direction of the coating close to zero. This resulted in better lifetimes for the TBC deposited via EB-PVD on cylindrical shape substrate . NeverthSputter-deposited thermal barrier coatings have been researched in very widely for longtime . Sputter2-7%Y2O3 have very high melting points (=2700 °C) and very low thermal conductivities. The PVD system requires a multiple step coating sequence process with precise controls. The function of the bond coat is to provide a stable base for adherence of the ceramic insulation layer. The bond coat must generate a stable, slow-growing alumina scale. In the PVD process the coating material is heated to vaporization temperatures through kinetic energy exchange between an electron beam (~38 kV) and the coating material . The proIt is estimated that the evaporation temperature at the point of electron beam impact is >5000 °C ,22,23. TThe slurry spray technique utilizes traditional wet powder spraying methods to deposit sinterable coating materials onto target substrates to be sintered to produce a functional coating. The process includes liquefying the coating material to form a slurry mixture that can be applied to a surface using a standard spray gun. Layers are sprayed in a sequential format onto the substrate. Then dried using different slurry compositions. The preferred thickness of the layers to prevent surface cracking during the drying process is approximately 0.25 mm and the drying time is about an hour, depending on the environmental conditions. After the desirable number of layers of the TBC is created, the multi-layered coating is pressed in a compression chamber to create a solid form before being sintered with an acetylene torch or furnace. The applied pressure varies depending on the number of coating layers, typically between 10 and 40 MPa . This coCVD is being developed as a TBC forming technique because of its non-light-of-sight nature and excellent conformal coverage characteristics. During the CVD process, a zirconium precursor such as Zr (dpm)4, and a yttrium precursor such as Y (dpm)3, are sublimed. The vapors are transported into a mixing chamber by a carrier gas of Ar. The secondary gas of oxygen is added into the mixing zone above the substrate [It should be noted that there are other procedures capable of coating ceramic and metallic materials. These procedures are older flame spray guns and the newer ones such as detonation guns , water-sNonetheless, these application methods have some flaws as well as advantages. For instance, the primary advantage of sputtering is its capability to deposit an extensive variety of materials .However, sputtering is yet to be considered as production method used for turbine engine components. This because the available equipment used in this process deposits the coating in a very slow manner. The physical vapor deposition process is an atomistic deposition method that encompasses the vaporization and subsequent deposition of coating species. It has the capability of delivering deposition of coatings of metals, superalloys, and ceramics on most materials and on a variety of shapes. It is a simple system to use.However, one deficiency in applying the traditional thermal spray techniques has been linked to having accurate deposition and thinness of the coatings. This makes the EB-PVD a preferred method in comparison to the plasma spray since a much smoother surface finished is attained as a result. Further, better strain tolerance, improved erosion-resistance properties, and better-quality metallurgical bonding with the substrate.Although in terms of erosion resistance and strain compliance the EB-PVD TBC systems has significant advantages over plasma sprayed TBC’s, in many applications in the gas turbine engine, such as the combustion chamber and nozzle guide vane platforms, plasma sprayed TBC performs sufficiently. These applications for plasma sprayed TBC’s will remain being used and because of their relatively low cost compared to EB-PVD TBCs, their application to these types of components in the future is somewhat guaranteed. Since EB-PVD technology proofed to be more effective than plasma spraying when depositing TBC on the airfoils of the modem gas turbine engines, where a good surface finish is vital and excellent strain compliance and adhesion are required. In these areas, EB-PVD offers significant benefits over plasma spraying which justifies the higher manufacturing costs of the EV-PVD. Pros and cons of most common and popular deposition technologies  are showOne of the major issue in EBC testing is the water vapor. Highly specialized laboratories with high steam capabilities  are usedPrediction and Accuracy: Once all the material properties for the reinforced ceramics (SiC/SiC) and TBCs/EBCs are available, it should be possible to simulate a simply supported beam for temperature and simulate how there is degradation in different materials and which one will fail first. A set of mechanical tests adopted by the American Society for Testing and Materials (ASTM) for materials characterization and durability assessments can be found in reference . These tCurrently, under steady state conditions, the failure phenomenon of the EBCs are not fully understood and models to estimate or predict the life of the EBCs are not available either. Thus, the need to develop modeling methodologies to determine and assess the life of the EBC and its durability is very essential to its success. However, factors that affect or influence the EBC stability, mechanisms of degradation and failure modes are to be determined and included in the modeling process. Predicting the durability and the life of the EBC alone and the CMC after the coating failure is very crucial to introducing CMC engine components coated with EBC.Hence, the need for modeling the elements that influence the performance of the EBC and the CMC is of high importance to engine makers and the aviation industry. In order for the CMC to be successful in the use of making engine components, the coatings that are applied have to offer a reliant means of protection. This translates into the fact that the life of the coated CMC is highly dependent on the life of the coating and not the uncoated substrate life. It further means that if the coating fails under certain condition, then then the CMC life is considerably decreased. This approach of design is in line with that of the TBC coated metal parts currently used in engines where the design life of TBC coated metal is purely dependent on the life of the uncoated part during engine operating conditions. Among the most critical issues ,32 that Other issues such as oxidation of the bond coat have been frequently identified as important factors affecting ceramic top-coat durability during service ,34,35. TConsequently, research to improve TBC durability and performance remains actively ongoing including a better understating of its service life . In the (1)Cracks spanning from the top coat through the thickness to the bond/intermediate coat interface and to the bond/substrate line with a formation and growth of horizontal crack alongside the interfaces. These cracks are linked and eventual spallation of the coating is imminent. Also, reactions at the interface and the formation of internal pores further accelerate the coating spallation. (2)Moisture through the coating to the substrate/bond coat interface and oxygen diffusion followed by formation of pores and oxidation of the substrate, linkage of the pore and eventual spallation of the coating. This observation is noted when a mismatch of thermal expansion between the substrate and the coating co-exist. An example of this mechanism is shown in Under therno-mechanichal cycling and due to foreign object damage, EBC can spall-off from the substrate. The failure mode can vary based on the operating conditions. Under thermal cycling condition without stress, two failure modes have been observed ,44:(1)CrIn the as-processed state, TBCs are well-bonded to the metallic substrate upon which they are deposited. However, as these coatings are exposed to high temperatures during turbine operation, they lose their adhesion. Therefore, in order to assess the durability of the TBC, certain tests have to be performed and developed to monitor and determine how these testing techniques, coupled with fracture mechanics principles can track the loss of adhesion and identify the mechanisms causing it.Tests are performed exposing the TBC to a variety of thermal exposures, including mechanism-based tests for isothermal dry air exposures, mechanism-based tests for exposures with water vapor and mechanism-based tests for cyclic thermal exposures. Therefore, TBCs and EBCs are more like monolithic ceramics (without fibers) and they do not need to be reverse engineered to predict the thermo-mechanical properties. They simply can be treated like metal (isotropic) and test data can be used to estimate the E, v, and strengths . Besides mechanical tests, the CTE variation of all the materials  with temperature, change has to be determined.(1)The need to develop modeling methodologies to determine and assess the life of the EBC and its durability is very essential to its success. However, factors that affect or influence the EBC stability, mechanisms of degradation and failure modes are to be determined and included in the modeling process.(2)In order for the CMC to be successful in the use of making engine components, the coatings that are applied have to offer a reliant means of protection. This translates into the fact that the life of the coated CMC is highly dependent on the life of the coating and not the uncoated substrate life. It further means that if the coating fails under certain condition, then then the CMC life is considerably decreased.(3)Predicting the durability and the life of the EBC alone and the CMC after the coating failure is very crucial to introducing CMC engine components coated with EBC.(4)Finally, a detailed representation of TBC and EBC physical characteristics along with their processing methodologies, testing procedures/approach and failure modes has been summarized. The focus on their failure modes and in particular the EBC is an essential element of this review since it is quite complex and modeling such aspects of failure modes requires extensive analytical/experimental testing efforts to include the causing factors. However, this is outside the scope of this article.Assessing the failure of TBCs and EBCs is not a simple task, and data from rigorous testing under severe and typical operating environments where factors that influence the characterization of these materials are needed. Thus, this paper offers a review and a detailed description of the materials features for both TBCs and EBCs citing in-depth discussion of their processing, applications and failure modes. Comparisons between the two coatings, their applicability, and the difference in their durability tolerance showing the substantial variance is demonstrated in a tabular format. Further, ample information about their materials characteristics and methods of developments and applications were recited and described in a rather detailed manner. Listed in the bullet points below are some fundamentals regarding the development of more conclusive life models for both EBCs and TBCs.


Sensory feedback is critical for grasping in able-bodied subjects. Consequently, closing the loop in upper-limb prosthetics by providing artificial sensory feedback to the amputee is expected to improve the prosthesis utility. Nevertheless, even though amputees rate the prospect of sensory feedback high, its benefits in daily life are still very much debated. We argue that in order to measure the potential functional benefit of artificial sensory feedback, the baseline open-loop performance needs to be established.EMG task), and grasping with a prosthesis (Prosthesis task). The subjects needed to activate the wrist flexor muscles and close the prosthesis to reach a randomly selected target level (routine grasping). To assess the baseline performance, the tasks were performed with a different extent of implicit feedback . Finally, the prosthesis task was repeated with explicit visual force feedback. The subjects’ ability to scale the prosthesis command/force was assessed by testing for a statistically significant increase in the median of the generated commands/forces between neighboring levels. The quality of control was evaluated by computing the median absolute error (MAE) with respect to the target.The myoelectric control of naïve able-bodied subjects was evaluated during modulation of electromyographic signals . In addition, the deviation of the generated commands/forces from the target levels decreased with additional feedback. However, the increase in implicit feedback, from proprioception to prosthesis motion and sound, seemed to have a more substantial effect than the final introduction of explicit feedback. Explicit feedback improved the performance mainly at the higher target-force levels.The study establishes the baseline performance of myoelectric control and prosthesis grasping force. The results demonstrate that even without additional feedback, naïve subjects can effectively modulate force with good accuracy with respect to that achieved when increasing the amount of feedback information. The hands are an essential part of our body and our most important tool to interact with the world. Not only do we grasp, move, and explore objects using the hands, but they also contribute to our interaction and communication with other living beings through the language of gestures. This refined control of the hands is due to a seamless integration of feedforward motor commands and an elaborate network of sensory feedback  we receive .The loss of a hand in people with transradial amputation can have a pronounced effect on the performance of daily-life activities and the general quality of life. The state-of-the-art in recovering hand function in persons with transradial amputation is to equip them with myoelectric prostheses. These systems are controlled through the activity of the hand and wrist flexor and extensor muscles in the residual limb, recorded by surface electromyography (sEMG). This is a robust and intuitive control scheme for simple, single degree of freedom grippers, since the muscles that were originally used to control the hand and wrist are now employed to open (extensors) and close (flexors) the prosthesis. However, the restoration of lost functions is only partial, as commercially available myoelectric prostheses do not provide explicit sensory feedback about the prosthesis state. The only exception is a recently presented system  providinSensory integration is crucial for motor execution, adaptation and learning . TherefoHowever, even in the absence of explicit force feedback from the prosthesis, the control is not completely “open-loop” since the users can still rely on incidental information sources. First, the prosthesis responds proportionally, that is, the stronger the contraction of the user muscles, the faster the velocity of closing and hence the higher the resulting contact force when grasping. Therefore, the user can rely on the proprioceptive feedback from the remaining muscles (sense of contraction) to control the force. In addition, the user can exploit visual feedback to estimate and adjust the velocity of closing, and thereby indirectly and predictively the resulting grasping force . This stProsthesis users can exploit implicit feedback sources in their daily use of the prosthetic device , 21. HowIn most studies, a specific solution was presented, and the prosthesis performance with feedback was compared to that without the feedback. These are important developments that can demonstrate the effectiveness of a particular feedback interface. However, such studies do not explicitly reveal the reasons why the presented feedback improved or failed to improve the performance. To address the latter, basic studies in controlled conditions should be performed to investigate the general nature of the mechanisms governing closed-loop control in prosthetics. The present study aims at closing that gap in the literature by investigating the role of incidental and explicit feedback sources in prosthesis control. We first determined the “open-loop” baseline and then compared it to the performance achieved using ideal explicit feedback. The subjects’ task was to control the magnitude of muscle activation and prosthesis grasping force. In the baseline condition, the subjects relied on natural proprioception from their muscles plus visual and audio cues related to prosthesis movement, whereas in the explicit feedback condition, the information on the generated grasping force was shown on a computer screen. The aim was to assess the impact of different amounts of implicit feedback on prosthesis force control and to investigate if and how much the performance can be improved by supplementing the implicit sources with explicit force information.Ten able-bodied subjects  participated in the experiment. Subjects were informed about the experiment both in writing and orally. The study was approved by the Ethics committee of the University of Göttingen (04/2016). All experiments were conducted in accordance with the declaration of Helsinki, and all participants provided written informed consent prior to participation in the experiments.The experimental setup consisted of: 1) a Michelangelo hand controlled proportionally using a single dry EMG electrode with embedded amplification, filtering and rectification circuit ; 2) a wooden block to be grasped by the prosthesis; and 3) a standard desktop computer with a 22″ screen. The control-loop was implemented in Matlab Simulink 2015b  using a flexible test bench for the assessment of the closed-loop human manual control  and execThe subjects sat comfortably in front of a desk with the computer screen positioned approximately 75 cm away from them Fig. . The proEMG task) or the level of grasping force when controlling the prosthesis (Prosthesis task). The grasping force was controlled in two tasks, first without (Prosthesis task without feedback) and then with an explicit visual force feedback shown on the computer screen (Prosthesis task with feedback). The generated and reference muscle activations and prosthesis forces were all normalized to the interval , as explained below.The experiment comprised three parts in which the subjects controlled two output variables, namely, the level of muscle contraction . The myoelectric signal proportionally controlled the velocity of closing and the resulting grasping force. The prosthesis input-output function was rather linear, so that the normalized myoelectric input produced approximately the same magnitude of the normalized grasping force . The subjects were instructed to activate the flexor muscles to the level that they estimate would lead to the desired grasping force and then maintain the contraction until the prosthesis closed . Here, the subjects were instructed to focus and rely on all the feedback cues available. In addition, the forces generated in this test established the baseline of force control with no explicit force feedback.In the ven Fig. . The aimProsthesis task with feedback the measured maximum force was shown on the computer screen, revealing to the subject the force that they were generating in that trial , followed by the Prosthesis task without feedback , and finally the Prosthesis task with feedback . This sequence was chosen so that, in each new task, the subjects were provided with additional feedback sources, thereby preventing unwanted across-task learning effects  was divided into six equal intervals, and the middle points of the intervals, from the second to the sixth, were adopted as the five target levels  and minimum value (just above zero) of the myoelectric command. To this aim, they were provided with visual feedback of the myoelectric command and asked to generate the respective values for several consecutive trials. Once they were sufficiently accurate in reaching them, they repeated the same task with eyes closed. The training was finished when they could generate the maximum/minimum myoelectric command five times in a row without relying on the visual feedback. At this point, it was assumed that the subjects had calibrated their control of muscle activation (EMG task). The subjects then performed the sequence of grasping trials. They were instructed to use the learned minimum/maximum values as a reference to scale their motor commands to the target level indicated in the trials. Importantly, before starting the Prosthesis task the subjects were introduced in detail to the basics of the prosthesis operation, including proportional control, the linear relation between closing velocity and generated force, and the non-backdrivability. The prosthesis closing and force control was demonstrated by closing it, via artificially-generated signals, at the maximum and minimum velocity for several times. The subjects were also allowed to close the prosthesis five times without receiving explicit force feedback, in order to get familiar with its control.Before starting the EMG task (generated myoelectric command) was the median of the generated muscle activation, computed over the last 1-s window of the 2-s trial duration  and the median EMG in the last 200 ms before touch onset (generated myoelectric command) were used as trial outcomes  in all tests described below.The trial outcome in the The goal of this analysis was to evaluate, separately for each of the three tasks, if the subjects could successfully scale their myoelectric commands and generated prosthesis forces according to the indicated target levels. To this aim, the medians of the generated muscle activations and prosthesis forces were determined for each target level and subject and statistically compared across target levels within the same task. A Friedman test was applied to evaluate statistically significant differences between levels 2 to 6. If present, Bonferroni-Holmes-corrected Wilcoxon signed-ranks tests were used to assess statistically significant differences between the neighboring levels (four tests per Friedman test). The analysis revealed how many statistically different levels of myoelectric commands and prosthesis forces the subjects could generate. In the ideal case, this would be equal to the number of target levels (five).Prosthesis tasks (with vs. without explicit feedback). The exact procedures for these two analyses are described in the remainder of this paragraph. Levels 2 and 6 were excluded as the prosthesis control was very different from the other levels. Namely, when aiming at level 6, the subjects often saturated both EMG command as well as the prosthesis force . Therefore, the true distribution of the generated myoelectric commands and prosthesis forces at level 6 could not be captured. For the target level 2, the subjects exploited the coupling inherent in the prosthesis, as explained in the section Experimental Protocol. They realized that to reach the 2nd level, they simply needed to produce the minimal myoelectric command to close the prosthesis at the minimum velocity, as the prosthesis could not generate lower forces. Therefore, they consistently undershot in the produced EMG , target level, and subject. To perform an overall analysis, the mean MAE across target levels was calculated for each subject and task. A Friedman test was applied to compare the overall performance in myoelectric command generation between the three tasks. If the test indicated significant differences, the tasks were compared pairwise using Bonferroni-Holmes-corrected Wilcoxon signed-rank tests. This analysis was performed to evaluate how the amount of feedback contributed to the control performance. Similarly, for the Prosthesis tasks the quality of force control (average MAE across levels 3 to 5) with and without explicit feedback was statistically compared using a Wilcoxon signed-rank test. To assess the force-control impact on individual levels, the same analysis was additionally performed separately for target levels 3 to 5.Myoelectric command generation was analyzed separately from force generation. The goal of this analysis was to assess if and how the amount of feedback altered the reproducibility of myoelectric commands and consequently the quality of force generation. To gain a more detailed insight into the impact of feedback in prosthesis force control, a level-wise force analysis was performed between the two see Fig. a2 and a3Prosthesis task, the subjects generated forces close to the target level while undershooting substantially with the myoelectric commands , the overall medians of the generated muscle activations and grasping forces significantly increased (p < 0.05 after Bonferroni-Holmes correction) from one target level to the next, for all the neighboring levels, clearly showing the subjects’ scaling ability. The range between the overall medians in the extreme levels was 66% for the EMG task, 88% in the EMG envelope and 74% in force for the Prosthesis task without feedback, and 76% in the EMG envelope and 71% in force for the Prosthesis task with feedback. The addition of the feedback sources seemed to have a beneficial effect on the scaling, as the dispersion of the subject-specific medians became lower, observable by comparing the interquartile ranges (the length of the boxplots), from Fig. Figure EMG task (22%), lower in the Prosthesis task without feedback (15%) and lowest in the Prosthesis task with feedback (12%). The pairwise differences were all statistically significant, as revealed by a Friedman test (p < 0.001) and significant post-hoc comparisons. Adding prosthesis motion and sound to proprioception significantly improved the command generation performance (EMG task vs. Prosthesis task without feedback), and providing explicit visual feedback improved the myoelectric control even further (Prosthesis task without feedback vs. with feedback). In the Prosthesis task, the improved myoelectric control also resulted in more accurate force generation, as the MAE of the generated forces decreased slightly but significantly when the explicit force feedback was introduced . The quality of force control for each individual target level is presented in Fig. Figure eedback 1% and lowThere have been no systematic studies so far assessing the baseline ability of human subjects to generate myoelectric commands and control a prosthesis force at a range of levels in “open-loop”, that is, with no explicit feedback on the grasp outcome. We have investigated incidental and explicit feedback in the context of prosthesis grasping. However, it is likely that the insights could be generalized to controlling other prosthesis functions. For example, the subjects might be able to control the wrist rotation speed  using muscle proprioception and motor sound, without even looking at the hand. Finally, it should be emphasized that implicit and explicit feedback are components of the larger human motor control scheme that needs to be considered in its totality in order to properly model and understand closed-loop prosthesis control. This includes, among others, the ability of the subject to learn, predict, and adapt , 28–30, Our results demonstrated that even in naïve users, the control without explicit feedback is not completely blind (open loop) but, on the contrary, surprisingly good. This could explain the unexpected outcome in some studies , 14, 25,EMG task only, this study shows that the feedback obtained through muscle proprioception is an important feature inherently available in myocontrol, essentially making myocontrol closed-loop control. This feedback source would not be present with other types of interfacing, such as nerve interfacing for motor commands without a target muscle. Using muscles for control is therefore very beneficial not just because they provide good control signals, but also because they provide very intuitive feedback.Looking at the EMG task to Prosthesis task without feedback) compared to when adding explicit feedback on top of prosthesis motion and sound (Prosthesis task without feedback to Prosthesis task with feedback). The improvement in myoelectric control due to explicit feedback translated into better force control, but this benefit was modest overall.The subjects could indeed generate five distinct levels, but they would not necessarily match the actual target levels, as the mean absolute error was between 10 and 20%. The quality of myoelectric control consistently improved with the addition of more feedback sources. Increasing the amount of implicit information, from proprioception only to proprioception and prosthesis motion and sound, and finally providing explicit feedback, steadily decreased the error in generating myoelectric commands. Interestingly, the improvement was more substantial when introducing the prosthesis . Instead, the explicit feedback was used intermediately, on a trial-to-trial basis. The subjects used the feedback to assess the task outcome, and based on the discrepancy between the intended and generated force level, they adjusted their motor command in the next trial. Finally, although the two tasks in the present study have been performed using a Michelangelo hand prosthesis, the general mechanisms of prosthesis operation  are common to most commercial systems and, therefore, allow us to interpret the study conclusions in a more general and broader relevance. Likewise, the first condition (EMG task), demonstrating the vital role of muscle proprioception during myocontrol, is fully device independent.However, it should be noted that the controllability in the Prosthesis task without feedback) suggests that force-feedback interfaces require a reasonably good resolution (< 15%) in order to have a chance to provide a benefit (increased performance with respect to the baseline), especially at the higher force levels. This is valuable information and should be considered in the future design of feedback interfaces.Generally, the good baseline performance . Nevertheless, this needs to be confirmed experimentally, as the controllability (fine vs. gross movements) and proprioception might be somewhat different.We only tested a homogenous group of naïve able-bodied subjects. Proprioceptive feedback from the muscles is also available to people with amputation, albeit somewhat different depending on the extent of amputation, shortening of muscles, and changes of attachment points. General skills as well as the interpretation and exploitation of the visual and auditory cues should be similar, and in addition, a person with amputation could exploit the incidental cues available through the prosthesis socket. Therefore, we expect that the results would not be significantly different in naïve persons with amputation, and even more so, the general conclusions regarding the importance of incidental feedback in prosthesis control. In experienced people with amputation, the baseline performance is likely to be even better than suggested here, due to extensive prosthesis use .It would certainly be interesting to further compare performance with prosthesis users, considering also their age and level of physical activity, as these factors might influence muscle proprioception , 37 but Introduction. Therefore, there was no background noise that could mask the prosthesis sounds, the hand was always clearly visible, and the task consisted of repeatable grasping of the same object. These conditions might facilitate the reliance on implicit feedback. In daily life, however, users will most likely encounter more challenging circumstances, which could make explicit feedback more important. For example, in our recent study, we have demonstrated that explicit feedback becomes relevant when the task complexity increases [Next, the present study has been purposefully conducted in well controlled laboratory conditions to obtain isolated insights into different feedback components, as explained in ncreases . Hence, This study provides a first systematic investigation of how implicit  and explicit feedback  interact to influence the performance of grasping force control. We showed that muscle proprioception alone already allows to scale the grasping forces. Adding the incidental feedback from the prosthesis improved the accuracy of this scaling by decreasing the deviations from the target levels. Importantly, the improvement was more substantial when increasing the amount of incidental feedback than when the explicit force feedback was introduced. These insights emphasize that a myoelectric interface already provides closed-loop control with good performance. Therefore, artificial feedback needs to be carefully designed to outperform this baseline and become truly effective.


A novel high-precision subdivision system for high-speed encoders is designed in this work. The system is designed with an arc second of Sin-Cos Encoder (SCE) based on zero phase bandpass filter. The system collects the analog output signals of an encoder with a high-speed data acquisition system (DAS); the noise of a digital signal can be effectively eliminated by zero phase bandpass filter with appropriate prior parameters. Finally, the actual rotation angle of the encoder is calculated by the software subdivision technique in the system. The software subdivision technique includes two methods, which are the Analog Pulse Counter (APC) and the Arc Tangent Subdivision (ATS). The APC method calculates the encoder angle by counting the analog pulses acquired by the arc tangent signal. The ATS method calculates the encoder angle by computing the arc tangent results of each point. The accuracy and stability of the system are first verified with a simulated signal; second, the real signals of an SCE are acquired by a high speed DAS on a test bench of a precision reducer, which is employed in industrial robots. The results of the proposed system are compared. The experimental results show that the system can significantly improve the accuracy of the encoder angle calculation, with controllable costs. In precision measurement and control systems with high dynamic and precision performance requirements, the position and rotational speed of the rotator must be accurately measured and perceived. The high-precision Sin-Cos Encoder (SCE) is a kind of sensor that converts the geometric displacement of the rotating shaft into a digital quantity . Based oThe encoder subdivision methods of the Moire fringe can be in organized into three categories: optical, mechanical, and electronic . Among tIt can be observed that in the current cosine encoder high-speed signal subdivision methods, the hardware subdivision methods achieve subdivision through the comparator circuit, which has the disadvantages of complex circuits, high cost, and poor flexibility. The software subdivision methods generally apply DSP or FPGA processors in the existing literature. These convert the encoder’s sin-cos signal into an AD sampling and conversion. Affected by the speed of operation, it can only subdivide according to the amplitude or the phase of the table subdivisions. Furthermore, the software methods cannot meet the real-time requirements of the test system for high-speed signal low-magnification subdivision requirements, due to the weak pulse processing capacity. In addition, the mechanical jitter caused by the operation of the motor is also extremely challenging, and it impacts the kind of filtering technology to adopt to remove noise. The accuracy of the calculation for the actual rotational speed of the rotor also needs to be further improved. In the case of low real-time demands, the software subdivision system in this paper uses the industrial computer as the upper computer processor of the subdivision operation. These computers can directly perform the advanced operations and subdivision of the sin-cos signal according to the algorithm without checking the table. The accuracy is higher and can support an effective filtering algorithm, such as the zero phase bandpass filtering algorithm, to remove noise.The operational principle of the SCE is similar to a common photoelectric encoder . The encAccording to the Shannon sampling theory , the samThe sin-cos encoding signal refers to the encoder signal that loads the position information with two phase sine signals, which have difference of 90° . A photoIdeally, the SCE outputs the two phases of an orthogonal voltage signal in a one-cycle rotation (phases A and B), as shown in The voltage signals of phase A and phase B can be expressed as follows.The principle of a software subdivision method can be expressed as follows; by selecting a point voltage s signal .An alternative software subdivision method is to sample the two sin-cos signals, and calculate the ratio of the two signals in order to obtain the phase value in a direct way. Theoretically, this method can calculate any position of the motor rotor. Moreover, the accuracy can theoretically achieve the actual physical precision of the encoder.(1)High-speed data acquisition system (DAS) collects signals.(2)Zero phase bandpass filtering algorithm removes the noise.(3)High-speed stream processing.(4)Software subdivision, including Analog Pulse Counter (APC) and Arc Tangent Subdivision (ATS), and using a parallel stream processing mode.The subdivision process of high-precision sin-cos encoding signal based on zero phase bandpass filter includes high-speed digitizer acquisition, zero phase bandpass filter, high-speed flow disk storage, and software segmentation. These elements, and the algorithm flow, are shown in The zero crossings can be estimated and hence the speed. So we can simply apply zero phase bandpass filter to the signal and then extract the phase using the Hilbert transform without any noise. Usually, the signal will have phase shift after filtering in time domain. Zero phase filtering is a digital filtering technology to solve this problem. The phase response of zero phase filter system function is zero. That is to say, there is no phase deviation after filtering . Zero phZero phase filter methods are as follows . First, The expressions of Fourier-transform in frequency domain are as follows.After the arrangement of Equations (7)–(10), it can be obtained thatAll in all, there is only an amplitude gain relationship between filter output ny noise .The APC method to generate the encoder output is through calculating the arctangent value of the signal, and then processing the analog signal. First, the phase angle signal arc tangent is computed; then the arc tangent value is used to simulate the pulse count. Finally, all of the pulse’s corresponding angles are superimposed to generate the output encoder rotation angle. The flowchart of the algorithm is shown in Arc tangent operation. According to the zero phase bandpass filter output result (phases A and B), the arctangent of each phase angle signal is calculated.In Equation (12), Tangent value overflow pretreatment. Because the tangent value of the signal neighbor at 90° approaches infinity, the overflow processing to the coded signal tangent value is going to overflow. Therefore, the overflowing tangent values are set to the overflowing threshold values. For example, the overflowing threshold values are Zero-crossing jitter elimination pretreatment. An SCE instrument error causes the sin-cos signal to exhibit the jitter phenomenon. A zero phase bandpass filter signal can still exist within smooth, vertical vibrations. When the judgment is abnormal fluctuation, the value Analog pulse counting operation. The subdivision ratio is Rotation angle output. The total rotation angle of the encoder is calculated according to the accumulator count value and the subdivision ratio:The ATS method generates the encoder output rotational angle by calculating the arctangent of the signal. First, the arctangent of the phase angle is calculated. Then, the starting phase angle, the total number of the half-period, and the signal endpoint phase angle of the signal are calculated. Finally, the encoder rotational angle is output. The algorithm flowchart is shown in Arc tangent operation and preprocessing. According to the output result of the zero phase bandpass filter (phases A and B), the arctangent of the phase angle signal of each point is calculated. The tangent value overflow preprocessing and zero-crossing vibration preprocessing is the same as Equation (12).Signal starting phase angle calculation. Because the signal does not always to start from half a period , as shown in The arctangent calculation. According to the zero phase bandpass filter output (phase A and phase B) in Analog pulse counting operation. Selecting the subdivision ratio The drop along the trigger threshold is set to a 4900 limit to eliminate the zero-crossing jitter elimination phenomenon. The phase angle signal arctangent responds to every pulse width to change the signal level and identify a complete pulse signal. The accumulator is used to obtain the pulse number Rotation angle output. Finally, the total rotation angle of the encoder is calculated according to the accumulator count value and the subdivision ratio:Based on the arctangent signal in Signal starting phase angle calculation. Since the signal does not necessarily start from the half cycle The peak of the deviation after the filtering is smaller than before the filter and the overall error decreases.(2)The standard deviation of the filter deviation is smaller than that before the filtering and the accidental error before the filtering is eliminated.(3)The negative overall tendency of the filtered deviation is due to the phase offset caused by the zero phase bandpass filtering algorithm. However, this offset has no effect on the relative rotation angle of the calculation.In the above test experiment of the high-precision SCE, the ATS and the APC methods are used. Compared with the actual theoretical angle, the deviation graph of the proposed software subdivision method is presented in Comparing the experimental results of the ATS method and the analogy pulse counting method, the influence of the two software subdivision methods on the accuracy of the calculation of the encoder rotation angle is analyzed. The results of the encoder angle comparison, presented in (1)The deviation of the ATS method is one order of magnitude lower than that of the APC method, and the accuracy is relatively higher.(2)The output from the APC method is a digital quantity. This value is affected by the system resolution and generates a pulse system error.In this paper, a novel subdivision system of the high-precision SCE based on a zero phase bandpass filter is designed, which can effectively solve the noise problem caused by the hardware and the environment. Two methods are explored: ATS and APC. The ATS method offers high precision and the APC provides a relatively low precision, but offers a fast, stable option for practical, real-time applications.In order to solve the problems of large signal noise and low subdivision accuracy in the high-speed sampling of encoder signals, this paper designs a software subdivision system based on zero phase bandpass filter for high-precision SCE. The conclusions can be summarized as follows.(1)According to the acquisition characteristics of high speed and high-precision SCE, a novel software subdivision system is designed. The method’s steps are straightforward, providing a solution that is fast, accurate, and easy to program.(2)Through the zero phase bandpass filter algorithm denoising, the filtered waveform tends to be stable and smooth, realizes the denoising effect, and effectively improves the angle error caused by the noise.(3)The ATS method can be directly used in the subdivision on the sin-cos signal, which is more practical in an offline situation and has higher accuracy.(4)By using the APC method, the converted pulse data can be imported into the embedded system to run. This provides better compatibility in comparison to the tangent subdivision method; it is more suitable for applications with stringent real-time requirements.Through the simulation analysis and the experimental verification, it is demonstrated that the proposed approach can be applied to the signal acquisition and the software segmentation of a high-precision SCE. The software subdivision method has the characteristics of high precision, easy integration, and low cost.In future, more research work should continually focus on the improvement for high-precision subdivision system. The research can also be used in the field of fault diagnosis and condition monitoring, such as reducer fault diagnosis or condition monitoring of railway vehicles and track. However, in most of complex systems, the monitoring data involves multiple types of parameters. Thus, a comprehensive method which can both handle the continuous and discrete parameters is essential for actual industrial applications. The presented system and its application to the envelope demodulation of reducer shock signal based on peak retention and downsampling might be developed by other groups of researchers which are developing creative methods of research, e.g., Kostrzewski .


The connected powertrain control, which uses intelligent transportation system information, has been widely researched to improve driver convenience and energy efficiency. The vehicle state prediction on decelerating driving conditions can be applied to automatic regenerative braking in electric vehicles. However, drivers can feel a sense of heterogeneity when regenerative control is performed based on prediction results from a general prediction model. As a result, a deceleration prediction model which represents individual driving characteristics is required to ensure a more comfortable experience with an automatic regenerative braking control. Thus, in this paper, we proposed a deceleration prediction model based on the parametric mathematical equation and explicit model parameters. The model is designed specifically for deceleration prediction by using the parametric equation that describes deceleration characteristics. Furthermore, the explicit model parameters are updated according to individual driver characteristics using the driver’s braking data during real driving situations. The proposed algorithm was integrated and validated on a real-time embedded system, and then, it was applied to the model-based regenerative control algorithm as a case study. The prediction of vehicle states based on Intelligent Transportation System (ITS) information can be widely used to improve energy efficiency and driver convenience ,2,3,4. FIn order to predict vehicle states, a number of studies on two methods have been typically introduced. The first method is a parametric model-based prediction. The intelligent driver model is a representative parametric model for vehicle-state prediction according to driver characteristics ,17,18,19The other modeling approach is the data-driven method using the driving data of each driver. The deep-learning ,21,22 anThe introduced research can predict the vehicle states with driver characteristics well. However, to apply the prediction algorithm for regenerative braking control, the algorithm should recognize diverse deceleration conditions such as stopping for a traffic light, slowing down for a speed bump, adhering to speed limits, or braking for a preceding vehicle. The algorithm should be specific for each deceleration condition. Furthermore, to reduce the sense of heterogeneity, the algorithm has to update the model parameters which represent the individual driver characteristics by online learning during real driving conditions. Most introduced data-driven algorithms require a premeasured data set to obtain the model and model parameters. Thus, typical data-driven methods are not appropriate for the online update algorithm.In order to solve these problems, we proposed a parametric deceleration model and online learning algorithm for its parameters in an earlier work. The proposed model predicts the vehicle deceleration profile when the vehicle is braking in front of traffic lights. This model consists of mathematical equations; those equations are converted depending on the physical situation and explicit model parameters that represent individual driver characteristics. The online learning algorithm updates explicit model parameters during real driving conditions because the algorithm was designed to require less calculation time and memory for the real-time embedded system. However, the previous model was designed for only the deceleration condition for traffic light stops and the algorithm runs the prediction algorithm when determining deceleration start conditions.This paper proposes the extended parametric deceleration model and Deceleration Condition Recognition Algorithm (DCRA) to cover diverse deceleration conditions. The operation process of the proposed algorithm is described at To sum contributions of the paper up, the algorithm can predict the deceleration-specified profile based on the proposed parametric model structure and can represent the individual driver characteristics to the predicted deceleration profile for diverse deceleration conditions. The proposed algorithm was validated through processes in the loop simulation using real-driving data. Subsequently, we analyzed and classified individual driver characteristics using the explicit model parameters of validation results. Furthermore, based on the proposed algorithm, the regenerative control was conducted as a case study in the car-following deceleration conditions.The rest of this paper is arranged as follows. In order to acquire driving data for designing the proposed algorithm, a Hyundai KONA Electric Vehicle (EV) was used. The experimental vehicle was equipped with additional sensors and equipment to acquire ITS information, as the proposed algorithm requires precise information of the driving environment. To recognize the deceleration conditions at the car-following situation, the vehicle measures the preceding vehicle velocity and relative distance between the two vehicles using the equipped radar sensor, as described in As mentioned in previous section, the precise positioning of the ego-vehicle and the static objectives are important to determine the deceleration conditions and, hence, to apply the prediction algorithm. To calculate the precise vehicle position, a localization algorithm which is introduced in References ,27 is apThe proposed algorithm consists of three parts, which can be seen in In the urban driving condition, a vehicle faces diverse driving states ,29. To aAt first, the driver intention machine checks the driver’s pedal operation. When the driver presses or releases the accelerator pedal, the driver intention machine determines the pedal transition state as an accelerator pedal transition state. It also gives the brake pedal transition state for the brake pedal operation, and both the accelerator and brake pedal positions are measured by the in-vehicle network.According to the pedal transition state of the driver intention machine, the driving state machine determines the driving state to affirm that the braking situation will occur. When the accelerator pedal is in the “off” state, the driving state machine sets the “coast start” state and checks the pedals’ off time. If the driver does not step on the accelerator pedal again during a specific period of time, the driving state machine will judge that the driving state is “brake ready”. This checking for pedal off-time can prevent the frequent state transition due to frequent acceleration. When the brake pedal is in the “on” state, the driving state machine gives a “brake start” state. The “brake termination” state then turns on when the driver releases their foot from the brake pedal. Finally, the state is determined as the “driving” state when the driver steps on the accelerator pedal.The deceleration condition machine classifies the deceleration condition if the driving state is determined as a brake-ready state. The deceleration condition is classified according to the deceleration source, which causes the braking situation in a driving environment. In this study, we determine the deceleration conditions to have two categories: dynamic deceleration and static deceleration. The dynamic deceleration condition is a condition where a vehicle follows the preceding vehicle. This state is determined using dynamic information such as the relative velocity and the relative distance. Dynamic information comes from vehicle-equipped sensors such as the acceleration sensor, the wheel speed sensor, and the radar sensor. If the preceding vehicle decelerates so that the relative velocity is negative, the current deceleration condition is in a car-following deceleration state.The static deceleration condition means the condition where the driver decelerates due to road objects on the driving route. The proposed algorithm selects three road objects: traffic lights, speed bumps, and road curvature. The algorithm can know the location of the traffic lights and speed bumps using ITS information like an HD map and GPS. Using this information, the distance to the road objects on the driving route can be calculated. Thus, when the driving state is brake ready and the relative distance to road objects are closer than configurable road-object distance criterion, the deceleration condition decides the vehicle will brake by the traffic light or the speed bump, respectively. The road curvature state is also another road object where a driver decelerates to reduce velocity to negotiate the curve without heterogeneity ,31. SimiFinally, the data logging state is determined as the data logging judgment machine for the learning algorithm. When braking starts, this state machine gives that the data logging state is on. During the data logging on state, the vehicle data and ITS information are stored. Then, the learning algorithm updates the model parameter using the stored data after braking is terminated.The deceleration prediction will be conducted based on the well-known Intelligent Driver Model (IDM) . This inIn this paper, we proposed parametric equations for the reference velocity and effective distance to predict deceleration situations more specifically. v is ego-vehicle velocity, and Furthermore, we proposed one more parametric equation that guarantees the end states for each of the deceleration conditions. That is a reference acceleration profile of Equation  which isCoasting section: The coasting section is the time range from when a driver releases the accelerator pedal to when the driver pushes the braking pedal.Initial section: In the initial section, the driver applies the brake pedal to reach the deceleration of the adjustment point. The driver decelerates with the jerk in this section. If the initial section is short and the jerk is large, the driver feels the rapid change of deceleration.Adjustment section: In the adjustment section, the driver applies a specific braking pedal force to maintain the deceleration without abrupt changes. The pattern of deceleration in this section depends on the driver’s characteristic and the deceleration state.Termination section: The termination section is the last part of the braking sections. Its definition also depends on driving conditions. In the static condition, the driver controls the braking pedal to satisfy the stop condition or minimum velocity in front of the static objectives. In the dynamic condition, the driver begins to remove the driver’s foot from the brake pedal.The deceleration data is analyzed to predict the deceleration profile based on drivers’ characteristics in each deceleration condition. Although the deceleration profile differs according to the driver’s driving style and deceleration conditions, the common feature in the declaration pattern is discovered. Therefore, we split the deceleration profile into four braking sections and its start points as shown in In order to represent the deceleration characteristics of the individual driver according to each determined braking section, explicit model parameters are determined. As mentioned in the previous section, these model parameters determine the time range of each braking section. The parameters also determine the parametric equations which are reference velocity and effective distance. Consequently, the vehicle acceleration in deceleration condition is predicted depending on the driver’s characteristics by model parameters affecting the braking section and parametric equations.The initial acceleration slope parameter represents the jerk state when braking starts. Since the jerk can cause uncomfortable sensations, this parameter should be treated as an individual driver parameter. The minimum velocity determines the braking termination condition. About the static deceleration condition, the minimum velocity is defined as a model parameter according to each object. The minimum velocity for traffic-light conditions is zero because the vehicle should stop in front of the traffic light. The minimum velocity for curve and speed-bump conditions are determined depending on the driving conditions and the driver’s driving characteristics. On the other hand, the minimum velocity of the car-following condition is defined depending on the preceding vehicle velocity.In addition, gain parameters determine the convergence of the predicted acceleration profile. When the braking progress is applied to the adjustment section, the acceleration profile converges to the reference acceleration because the driver follows the stop condition. The relative distance parameters affect the transition time from the initial section to the adjustment section. The detailed process of the braking section transition and transformation of parametric equations is described in the next section.As mentioned above, the deceleration prediction model calculates the deceleration profile using parametric equations of the reference velocity The descriptions of parametric equations for each braking section are explained as follows. In the coasting section, the model predicts the coasting deceleration with value In the initial section, the model predicts the jerk characteristics when braking starts. In this section, the effective distance is also set to zero, likewise in the coasting section. The reference velocity is calculated according to the acceleration slope When the adjustment section starts, the driver normally adjusts the brake pedal to converge to the reference acceleration profile. To represent this convergence, the effective distance is determined using the acceleration error value between the predicted acceleration and the calculated reference acceleration profile. Since the acceleration is calculated by the ratio between the effective distance and relative distance, the initial value of effective distance is set to the adjustment relative distance. Then, the effective distance is adapted depending on the adjustment gain. This adjustment gain is defined differently depending on the respective deceleration conditions. The reference velocity is also calculated to keep the velocity ratio to the estimated velocity when the adjustment section starts. Finally, the adjustment section is finished when the acceleration reaches the reference acceleration.When the braking is almost over, the driver controls the brake pedal to satisfy specific safety conditions. In the static deceleration conditions, the driver controls the acceleration to trace the reference acceleration to satisfy the minimum velocity at the front of the static object. On the other hand, the driver tends to keep a relatively safe distance according to the current vehicle speed of the car-following deceleration state. To guarantee these specific safety conditions, the model updates the effective distance similarly to the adjustment section using the termination gain parameter. When the vehicle velocity is slower than the minimum velocity or preceding vehicle velocity, the termination section is finished.Model parameters are used to determine parametric equations and braking-section transition of the deceleration-prediction algorithm. Thus, the deceleration-prediction algorithm predicts the various deceleration profiles depending on the parameter values. The proposed online learning updates these model parameters to reflect individual driver characteristics for various deceleration conditions. This algorithm consists of two parts; parameter activation and parameter update as shown in The parameter-activation algorithm selects the parameter values of each model parameter using their learned vector arrays when deceleration starts according to the deceleration states. Then, the parameter-update algorithm updates the vector array value of each parameter using the reference parameter. The reference parameter is determined using the driver’s braking data after deceleration is finished.In order to manage parameters according to the deceleration states, the parameter values are defined as the vector array value depending on each vector index. The deceleration state which affects the selection of the parameter value is defined as the vector index of the vector array such as the initial deceleration condition, relative distance to the object, or ego-vehicle velocity. This vector index for each parameter is determined as a correlated deceleration state to its model parameter value. Thus, the parameter activation algorithm can select appropriate parameter values depending on the deceleration states. For example, a maximum acceleration parameter is highly correlated to the coast acceleration indicator for all drivers, which can be seen in The initial jerk parameter, which is an acceleration differential value at the initial section, is also defined as a model parameter with the index of the initial acceleration indicator. As shown in In the dynamic deceleration condition, a driver’s driving style is affected based on the preceding vehicle behavior. Therefore, the model parameters are determined as relative states from the preceding vehicle. The relative distance of the initial section is determined by the relative distance of the coast section as shown in To use the model parameters for the prediction model, the parameter activation algorithm selects the parameter value of each parameter from the its parameter vector array value. The selected parameter value should be related according to the relevant deceleration states. In addition, the parameter value is treated as stochastic because the parameter vector is updated based on the data-driven method .i, As mentioned above, the vector index is correlated with the parameter vector and represents deceleration states. Thus, the parameter activation algorithm selects the parameter value by selecting the highly correlated vector index to the current deceleration states. To obtain the degree of relation between the vector index and deceleration states, an effective likelihood is determined by normalizing the Gaussian distribution for each index value as can be seen in Equation .(4)P(i)For example, when the acceleration indicator is calculated as a 2.1 value according to the deceleration states as shown in In order to update the vector value of each parameter to reflect individual driver characteristics, the online learning algorithm calculates the reference values of each parameter using the braking data of the driver. At first, the transition points of each braking section are determined because the proposed model parameters are determined related to the braking section transition. For example, the adjustment acceleration is determined as the acceleration value when the adjustment section starts. The initial jerk is also determined using the acceleration values between the start point of the initial section and start point of the adjustment section.The transition times for the coasting section and the initial section are determined easily using the pedal transition information of the driver. When the driver releases the acceleration pedal, the coasting section starts. Then, the initial section starts when the driver pushes the brake pedal. To determine the transition times for the adjustment section and termination section, the reference acceleration and reference velocity are calculated using the measured braking data. These two profiles are described in the prediction model section. The reference velocity is calculated based on the IDM equation to find the adjustment point. By setting the effective distance as a zero value and by using the minimum velocity parameter, the reference velocity profile Equation . Then, tReference variables of relative distance parameters are defined by using the measured relative distance from the radar sensor at each section transition point. The reference value of the minimum velocity parameter is defined using the measured vehicle velocity during the braking conditions. Similarly, the reference value of maximum acceleration is also defined using the measured vehicle acceleration.After calculation of the reference value using the braking data, the online learning algorithm updates the vector array value. The error value is calculated using the activated parameter value Equation . ThroughEquation .In this process, the update algorithm applies the learning degree quations  and 10)ψ(i), whiquations  is derivquations  and 8)..ψ(i), whThe proposed algorithm was validated through the vehicle experiment. To validate the static deceleration condition, the algorithm has to know the exact position of the objectives and its status. Since the predefined HD map can provide the position of objectives, the validation experiment for the static deceleration condition was conducted at a specific test cite. The proving ground is Yeongjongdo in Incheon, South Korea, whereby this site contains a speed bump, traffic light, and curvature road conditions to successfully carry out experiments. The algorithm for the dynamic deceleration condition in the car-following situation was validated in various driving conditions for urban driving and highway-driving environments. To specify the driving characteristics for the individual driver, three drivers conducted the vehicle experiments according to the case listed above. The purpose of the proposed research is to present a predictive model for various deceleration conditions and to explicitly update the driving characteristics of individual drivers and not to macroscopically classify them using machine learning methods. Therefore, even if a model is defined and validated using a small number of driver’s data, the model is valid if it can clearly show the individual driver’s characteristics for various deceleration conditions.2. The prediction model cannot represent this strong braking at an early braking state. The model does not work at deceleration cases around 100 s of bump condition because the deceleration occurs due to other reasons and not just due to the speed bump.The deceleration model about dynamic conditions was validated through proving ground driving and real road driving. Real road driving contains various urban and highway driving environments. In the proving ground results, the vehicle deceleration occurred by deceleration of a preceding vehicle. The results shown in As mentioned in the parameter management section, the model parameter values are managed as vector values according to the highly correlated index. At first, using the vehicle experiment data for all drivers, we determined the base parameter vector to apply the proposed learning algorithm. As shown in the algorithm overview in Parameter vectors of the static condition deceleration model are updated according to each deceleration condition for the individual drivers. The proposed algorithm was applied to the smart regenerative control system of an electric vehicle as a case study. The controller structure is shown in The torque control algorithm consists of two controllers: the feedforward controller and the feedback controller. The feedforward controller determines the motor torque based on the electric vehicle model. Using the electric vehicle model, the feedforward controller calculates the desired regenerative motor torque to generate the acceleration set point. The feedback controller is a well-known Proportional-Integral-Derivative controller (PID controller) to trace the acceleration set point from the prediction model.The Vehicle Control Unit (VCU) controls the motor torque according to the generated regenerative torque from the control algorithm. Consequently, the vehicle decelerates without the driver’s braking pedaling action. Thus, it provides driving convenience by excluding pedaling of the vehicle’s brake. In this paper, we proposed a deceleration prediction model based on individual driver characteristics. The proposed prediction model is designed based on the mathematical intelligent driver model, and it is modified especially to the deceleration prediction with some explicit model parameters. First, we defined the braking section that describes the deceleration characteristics to specifically predict the deceleration state. Then, the parametric equations were calculated according to the braking section with explicit model parameters to predict the deceleration profile. The model parameters represent the individual driver characteristics by updating the vector array values online. These vectors are also updated according to the various deceleration conditions about the various road objects or preceding vehicle. Thus, the proposed model can be used for various deceleration conditions by considering the individual driver’s characteristics. The proposed algorithm was validated through vehicle experiments and applied to smart regenerative control. Since the model can execute various deceleration conditions and driver characteristics, the smart regenerative control based on the proposed model does not cause the sense of heterogeneity for an individual driver. In future research, the proposed algorithm will be applied to smart regenerative systems on real driving situations. To achieve this, the deceleration condition recognition algorithm will be modified more practically. The algorithm can substitute the navigation device for the HD map and GPS to determine information of road objectives. Furthermore, the dynamic deceleration condition also is extended to consider traffic jam or cut-in situations.