Position Constraints Adaptive Iterative Learning Control of Exoskeleton for The Preliminary Stage of Rehabilitation

In this paper, the trajectory tracking control problem of an exoskeleton using in the preliminary stage of rehabilitation is addressed via adaptive iterative learning control. The disturbances are handled by radial basis function neural networks. A Lyapunov-like barrier composite energy function is utilized to ensure that position constraints are satisfied. With the help of the proposed control method, the exoskeleton leg can achieve good tracking performance without violating output constraints. Convergence of system tracking errors is demonstrated by mathematical method and performance of the proposed controller is illustrated by numerical simulations.