A learning control scheme for upper-limb exoskeleton via adaptive sliding mode technique

Abstract

This paper investigates an iterative learning approach integrated with sliding mode control method to accomplish passive rehabilitation therapy tasks for wearable 6 degrees of freedom (DOF) upper-limb exoskeleton. Firstly, the motion data of human body is collected from a healthy subject through VICON motion capture system and reasonable trajectories in joint space are generated by fitting functions. Secondly, an iterative learning controller is developed to estimate the iteration-invariant dynamic parameters which are complicated and difficult to be obtained precisely in practice. Note that the identical initial condition (i.i.c) in traditional iterative learning control (ILC) is released by applying the polynomial reconstruction method. Considering the uncertainties and disturbances which affect the system in the form of friction, backlash and unexpected tissue torques from human body, an adaptive law is proposed to estimate the upper bound of the lumped non-periodic disturbances. Based on that, sliding mode controller is conducted to achieve the robustness over the time domain, while the chattering phenomenon is attenuated by applying tanh function. Afterwards, the stability and convergence of the overall system is rigorously proved with a composite energy function (CEF) composed of tracking and estimating errors. Finally, co-simulation and experiment results are presented to demonstrate the effectiveness of the proposed control scheme.