Adaptive echo state network control for a class of pure-feedback systems with input and output constraints

In this paper, an adaptive echo state network control scheme is proposed for a class of constrained pure-feedback systems, in which both input and output constraints are considered simultaneously. A prescribed performance function characterizing convergence rate, maximum overshoot and steady-state error is employed to enhance the transient tracking performance. Moreover, an improved dynamic surface sliding mode approach is developed by incorporating high-order sliding mode (HOSM) differentiators into each step of controllers design, such that the sluggish effect of the filter performance in conventional dynamic surface control (DSC) can be eliminated. Finally, the unknown nonlinearities including the input saturation dynamics are estimated by using an echo state network, which is easily trained without adjusting the weights between the input layer and the hidden layer. Comparative simulations are provided to illustrate the effectiveness and superior performance of the proposed method.