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Human Model Reference Adaptive Control of a Prosthetic Hand

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Abstract

A human model reference adaptive controller (HMRAC) is developed for a prosthetic hand. The model reference for the adaptive controller is formed from grasp experiments with human test subjects. This HMRAC is incorporated within a hybrid force-position control law; electromyogram (EMG) signals from amputee and nonamputee test subjects are used to control the force or position of the prosthetic hand. The HMRAC is compared to a sliding mode controller (SMC) with high and low control gains during bench top experiments with step and EMG inputs while grasping high and low stiffness objects. Results from the bench top experiments show that the SMC with a high control gain produced the least amount of tracking error with the EMG input at the expense of a highly oscillatory system response while grasping the high stiffness object. The HMRAC produced less tracking error with the step inputs in all cases and less tracking error with the EMG input in comparison to the SMC with a low control gain. The HMRAC also produced less percent overshoot (OS) with the step inputs on average in comparison to the SMC in all cases. Experiments were also performed by a transradial amputee with the SMC and the HMRAC. Both controllers were compared to the amputee’s current prosthesis for daily use. Results from the experiments performed by the amputee with the HMRAC and the SMC were similar to the bench top experiments: the high gain SMC had the least tracking error on average at the expense of a highly oscillatory system response with high object stiffness. The HMRAC was not oscillatory and had the next lowest amount of tracking error than all other prosthesis control options. The HMRAC had slightly more error than the amputee had while using his natural left hand. Similar results were obtained from seven nonamputees who also participated in this study.

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Correspondence to Erik D. Engeberg.

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Engeberg, E.D. Human Model Reference Adaptive Control of a Prosthetic Hand. J Intell Robot Syst 72, 41–56 (2013). https://doi.org/10.1007/s10846-013-9815-9

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  • DOI: https://doi.org/10.1007/s10846-013-9815-9

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