Neural adaptive control for uncertain MIMO systems with constrained input via intercepted adaptation and single learning parameter approach

Abstract

This paper focuses on the problem of semi-globally stable neural adaptive control for a class of uncertain multi-input/multi-output nonlinear systems in the presence of strong interconnection, input saturation, and external disturbance. Radial basis function neural networks are utilized in the online learning of uncertain dynamics. The features of the scheme developed can be briefly summarized as follows: (1) The problem of “explosion of complexity” caused by the repeated differentiations of virtual controllers in traditional backstepping design is circumvented via the pioneering dynamic surface control technique; (2) the subsystem in the whole system can be any order, and only one scalar is needed to be online updated when dealing with uncertain dynamics and external disturbance, which is computationally inexpensive from the perspective of practical application; and (3) the bounds of transient and ultimate tracking errors are adjusted by the design parameters in an explicit form with input saturation in effect by virtue of the novel intercepted adaptation approach. It is proved via Lyapunov stability theory that all the closed-loop signals are guaranteed semi-globally uniformly ultimately bounded, and simulation results are presented to verify the effectiveness of the proposed method.