Single-Layer Learning-Based Predictive Control With Echo State Network for Pneumatic-Muscle-Actuators-Driven Exoskeleton

This article presents a single-layer learning-based predictive control strategy for pneumatic muscle actuators (PMAs)-driven lower limb exoskeleton. Although PMAs are promising for rehabilitation robots, they suffer from nonlinearities, unmodeled uncertainties, hysteresis, etc. As a consequence, the mechanism actuated by PMAs rarely involves complex dynamics, and the related precise control remains a challenging problem. Hence, considering the global approximation capability of neural networks, we use an echo state network (ESN) to approximate the dynamics of the PMAs-driven exoskeleton with a nonlinear autoregressive exogenous model and forecast its behaviors by constructing training and testing data sets. Through the model predictions, the idea of single-layer learning solves a quadratic programming problem based on the principle of predictive control over a finite future horizon. After that, the control strategy turns out to be asymptotically stable when the ESN is capable of approximating the dynamics of the exoskeleton. Passive gait training experiments are conducted with six healthy subjects to verify the effectiveness of the proposed control strategy. Compared with the traditional strategies, the proposed control strategy achieves higher tracking accuracy for passive gait training tasks.