Any implant or prosthesis replacing a function or functions of an organ or group of organs should be biologically and sensorily integrated with the human body in order to increase their acceptance with their user. If this replacement is for a human hand, which is an important interface between humans and their environment, the acceptance issue and developing sensory-motor embodiment will be more challenging. Despite progress in prosthesis technologies, 50–60% of hand amputees wear a prosthetic device. One primary reason for the rejection of the prosthetic hands is that there is no or negligibly small feedback or tactile sensation from the hand to the user, making the hands less functional. In fact, the loss of a hand means interrupting the closed-loop sensory feedback between the brain (motor control) and the hand (sensory feedback through the nerves). The lack of feedback requires significant cognitive efforts from the user in order to do basic gestures and daily activities. To this aim, recently, there has been significant development in the provision of sensory feedback from transradial prosthetic hands, to enable the user take part in the control loop and improve user embodiment. Sensory feedback to the hand users can be provided via invasive and non-invasive methods. The latter includes the use of temperature, vibration, mechanical pressure and skin stretching, electrotactile stimulation, phantom limb stimulation, audio feedback, and augmented reality. This paper provides a comprehensive review of the non-invasive methods, performs their critical evaluation, and presents challenges and opportunities associated with the non-invasive sensory feedback methods.

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A Review of Non-Invasive Sensory Feedback Methods for Transradial Prosthetic Hands

Permanent magnet synchronous motor (PMSM) drive has emerged as one of the most preferred motor drives for industrial applications owing to its distinguished advantages, such as high torque density, high efficiency, and wide speed range operation. However, accurately updated knowledge about the PMSM parameters is crucial to designing a high-performance trajectory tracking controller. This article presents a state-of-the-art comprehensive review and up-to-date progress of the offline and online parameter estimation techniques of the PMSM drives. First, the problems associated with parameter estimation of the PMSM are systematically summarized to identify the parameters having fast and accurate convergence. Second, the offline parameter estimation techniques are categorized based on the frequency and time responses. Third, the recent advances and developments of the online parameter estimation techniques for PMSMs are assessed. Finally, there is a discussion of extensive industrial applications and future research trends for the parameter estimation techniques.

A Comprehensive Review of State-of-the-Art Parameter Estimation Techniques for Permanent Magnet Synchronous Motors in Wide Speed Range

Neural network based MPPT control with reconfigured quadratic boost converter for fuel cell application

OBJECTIVE A variety of bioengineering systems are being developed to restore tactile sensations in individuals who have lost somatosensory feedback because of spinal cord injury, stroke, or amputation. These systems typically detect tactile force with sensors placed on an insensate hand (or prosthetic hand in the case of amputees) and deliver touch information by electrically or mechanically stimulating sensate skin above the site of injury. Successful object manipulation, however, also requires proprioceptive feedback representing the configuration and movements of the hand and digits. APPROACH Therefore, we developed a simple system that simultaneously provides information about tactile grip force and hand aperture using current amplitude-modulated electrotactile feedback. We evaluated the utility of this system by testing the ability of eight healthy human subjects to distinguish among 27 objects of varying sizes, weights, and compliances based entirely on electrotactile feedback. The feedback was modulated by grip-force and hand-aperture sensors placed on the hand of an experimenter (not visible to the subject) grasping and lifting the test objects. We were also interested to determine the degree to which subjects could learn to use such feedback when tested over five consecutive sessions. MAIN RESULTS The average percentage correct identifications on day 1 (28.5%  ±  8.2% correct) was well above chance (3.7%) and increased significantly with training to 49.2%  ±  10.6% on day 5. Furthermore, this training transferred reasonably well to a set of novel objects. SIGNIFICANCE These results suggest that simple, non-invasive methods can provide useful multisensory feedback that might prove beneficial in improving the control over prosthetic limbs.

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Object Discrimination Using Electrotactile Feedback

Supernumerary robotic limbs (SRLs) are a new type of wearable human auxiliary equipment, which is currently a hot research topic in the world. SRLs have broad applications in many fields, and will provide a reference and technical support for the realization of human-robot collaboration and integration, while playing an important role in improving social security and public services. In this paper, representative SRLs are summarized from the aspects of related literature analysis, research status, ontology structure design, control and driving, sensing and perception, and application fields. This paper also analyzes and summarizes the current technical challenges faced by SRLs, and reviews development progress and key technologies, thus giving a prospect of future technical development trends.

Review of Research and Development of Supernumerary Robotic Limbs

This study proposes an advanced current and position sensor fault diagnosis mechanism for permanent magnet synchronous motor (PMSM) applications. Machine nonlinear dynamics as well as parameter and load uncertainties are addressed. The features of the results of this study are summarized as follows: 1) a proportional-type full-state observer is combined with a disturbance observer to deal with the model uncertainties, 2) a partial optimal process is introduced for determining the observer gain, 3) the concept of normalized residuals is introduced for fault diagnosis criteria, and 4) a closed-loop analysis of the convergence, performance recovery, and offset-free guarantee is provided. The performance recovery property makes the proposed algorithm robust to parameter uncertainties and load variations, and sensitive only to sensor faults. In addition, the location and type of sensor faults can be identified by the fault diagnosis criteria using the normalized residuals. The performance of the sensor fault diagnosis algorithm was experimentally verified under several credible scenarios using a 7.5-kW PMSM.

Current and Position Sensor Fault Diagnosis Algorithm for PMSM Drives Based on Robust State Observer

In this paper, a new method is proposed for the real-time estimation of the full parameters of a permanent magnet synchronous motor (PMSM) that is based on the stator current ripple model. By introducing the stator current ripple model together with the known two state equations for the $d$ - and $q$ -axes currents, we resolve the rank deficiency problem, thus enabling real-time full parameter estimation even in the steady state. A signal processing technique for removing adverse effects from noises and uncertainties existing in the stator current ripple measurement is also presented. The efficacy of the proposed method is verified by simulation on a 15-kW PMSM, and both the proposed and conventional methods are compared.

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Using the Stator Current Ripple Model for Real-Time Estimation of Full Parameters of a Permanent Magnet Synchronous Motor

Electrotactile stimulation has been used to provide sensory information of forearm prosthesis to users. Although conventional sensory feedback method, where one electrode expresses sensory information of only one finger, could provide force information of three fingers by using three electrodes, it showed less cognitive accuracy when more than two electrodes were stimulated simultaneously compared to individual stimulation. To improve the cognitive accuracy, we presented a sensory feedback method called two-channel electrotactile stimulation for the thumb, index and middle fingers using only two electrodes. The force information of the index and middle fingers was delivered into two electrodes, respectively, by intermittent stimulation. The information of the thumb was delivered to the user by inserting additional offset pulses onto the channel of the index finger based on an assumption that the force of thumb is proportional to the sum of the forces of the index and middle fingers. The presence of offset pulses in the channel indicates the binary state of thumb if it contacts with an object or not. We conducted two psychophysical experiments where healthy subjects classified the binary states of each finger and identified intensity from two-channel stimuli. The cognitive accuracies for intensity identification were 78.8% and 62.2% for the intermittent stimulation and conventional method, respectively, and accuracy for classifying the fingers was 93.1% for every combination of the three fingers. The results demonstrated that the proposed two-channel electrotactile stimulation could be an attractive method to express the information of fingers of prosthesis with high accuracy.

Two-channel electrotactile stimulation for sensory feedback of fingers of prosthesis

One of the long-standing challenges in upper limb prosthetics is restoring the sensory feedback that is missing due to amputation. Two approaches have previously been presented to provide various types of sensory information to users, namely, multi-modality sensory feedback and using an array of single-modality stimulators. However, the feedback systems used in these approaches were too bulky to be embedded in prosthesis sockets. In this paper, we propose an electrocutaneous sensory feedback method that is capable of conveying two modalities simultaneously with only one electrode. The stimulation method, which we call mixed-modality stimulation, utilizes the phenomenon in which the superposition of two electric pulse trains of different frequencies is able to evoke two different modalities (i.e., pressure and tapping) at the same time. We conducted psychophysical experiments in which healthy subjects were required to recognize the intensity of pressure or the frequency of tapping from mixed-modality or two-channel stimulations. The results demonstrated that the subjects were able to discriminate the features of the two modalities in one electrode during mixed-modality stimulation and that the accuracies of successful recognitions (mean ± standard deviation) for the two feedback variables were 84.3 ± 7% for mixed-modality stimulation and 89.5 ± 6% for two-channel dual-modality stimulation, showing no statistically significant difference. Therefore, mixed-modality stimulation is an attractive method for modulating two modalities independently with only one electrode, and it could be used for implementing a compact sensory feedback system that is able to provide two different types of sensory information from prosthetics.

Mixed-Modality Stimulation to Evoke Two Modalities Simultaneously in One Channel for Electrocutaneous Sensory Feedback

The equivalent consumption minimization strategy (ECMS) has been considered as a practical energy management strategy for the hybrid electric vehicles (HEVs) because it can be implemented in real time while providing satisfactory performance. However, it is still challenging to adjust an equivalent factor (EF) to its own optimal value in real time because the EF is fundamentally affected by the current driving condition. Although many adaptive ECMSs (A-ECMSs) have been developed to adjust the EF based on a charge-sustaining condition, they do not adequately respond to a change in the driving conditions. In this study, a novel ECMS for the HEVs is proposed to provide the near-optimal performance by considering actual driving conditions. First, the near-optimal condition for the EF is defined to consider a driving condition. Based on it, an iterative scheme is presented to numerically obtain the near-optimal EF. Then, the convergence analysis of the iterative scheme is conducted with practical considerations to implementing the proposed method into real-world applications. Simulation results show that the proposed strategy has better adaptability to changes in the driving conditions with a smaller loss of optimality than conventional A-ECMS which relies only on the charge-sustaining condition. The proposed strategy is also experimentally validated under real-world driving conditions.

Kyunghwan Choi

Papers

Current and Position Sensor Fault Diagnosis Algorithm for PMSM Drives Based on Robust State Observer

Using the Stator Current Ripple Model for Real-Time Estimation of Full Parameters of a Permanent Magnet Synchronous Motor

Two-channel electrotactile stimulation for sensory feedback of fingers of prosthesis

Mixed-Modality Stimulation to Evoke Two Modalities Simultaneously in One Channel for Electrocutaneous Sensory Feedback

Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) for the HEVs with a Near-Optimal Equivalent Factor Considering Driving Conditions