Adaptive Neural Network Control for Exoskeleton Motion Rehabilitation Robot With Disturbances and Uncertain Parameters

Abstract

This paper investigates the adaptive control problem for a class of Euler-Lagrangian (EL) systems with uncertain parameters and external disturbances. While the EL system is one of the most classical system models, the dynamics of exoskeleton motion rehabilitation robots (EMRR) can be modeled as an EL system by the Lagrangian approach. First, in the absence of uncertainties, it is shown that an effective controller can be designed easily such that the system state tracks the reference signal. Second, for the case where uncertainties exist, a neural network (NN) is introduced to approximate the uncertain terms, and a novel adaptive controller design strategy is proposed to improve the resilience against disturbance by taking H∞ performance into account. Moreover, based on the constructed adaptive controller, an effective stability analysis technique is also proposed for the cases where intermittent measurements are collected. Finally, an EMRR system example is provided to demonstrate the effectiveness of the proposed method.