Event-Triggered Neural Tracking Control of Cable-Driven Lower-Limb Exoskeleton Robots

This paper gives an event-triggered neural tracking control strategy for cable-driven lower-limb exoskeleton robots. To reduce the number of triggers and prevent the jitter of the control signal, a switching threshold is used to drive the event trigger, which is different from the traditional trigger cases of fixed and relative thresholds. Firstly, a neural network (NN) is adopted to approximate the unknown nonlinear function, with the extracted estimation error being new leakage terms developed to update the weight of NN. Then an adaptive controller is designed to achieve the uniformly ultimately bounded(UUB) performance of the closed-loop system, and at the same time both transient and steady-state performance of the tracking error is guaranteed via a predefined performance function. Finally, the effectiveness of the proposed method is verified by simulation.