Tremor suppression by FES using closed-loop control

In this work tremor suppression by functional electrical stimulation is described, using repetitive and iterative learning control algorithm. Multibody and muscle dynamics are exploited to obtain models of the human arm. Furthermore, for first evidence and realistic feasibility the wrist joint is chosen for tremor suppression. System parameters are obtained by measurements and frequency decomposition. For simulations robust high order repetitive control, optimality-based repetitive control and norm-based iterative learning control are applied. For experiments optimality-based repetitive control is applied, using a wrist setup for signal processing. The simulations and experiments are executed for tremor stabilization and reference tracking