Iterative learning control of an exoskeleton for human upper limbs

The paper presents iterative learning control of an exoskeleton for human upper limbs to provide forearm movement assistance, which enables a human forearm to track any continuous desired trajectory (or constant setpoint), in the presence of parametric/functional uncertainties, unmodelled dynamics including actuator dynamics, and/or disturbances from the environment. Given desired trajectories of human forearm positions, iterative learning control scheme is to compensate for unknown time-varying periodic disturbances. Using iterative learning and Lyapunov synthesis approaches, the developed controller does not require exact knowledge of muscle model and can be proven to be semiglobally uniformly bounded for the close loop system. The performance of the controller is proven by laboratory tests. The two comparison experiments are conducted to illustrate the effectiveness of the proposed control by track tracking periodic/repeated trajectories.