Abstract
This paper focuses on the application of model-based predictive control (MPC) for a full wrist exoskeleton designed for the alleviation of tremors in patients suffering from Parkinson's Disease and Essential Tremor. The main motivation for using MPC here relies on its ability to incorporate state and input constraints, which are crucial for the user's safety. The forearm-exoskeleton model is successively linearized at each time sample to obtain a linear state space model. The optimal input is then generated by minimizing a convex quadratic cost function. Finally, simulation cases are provided to demonstrate the effectiveness of the control scheme.
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