Event-triggered fixed-time adaptive neural dynamic surface control for stochastic non-triangular structure nonlinear systems

Abstract

The problem of event-triggered fixed-time adaptive neural dynamic surface control (DSC) for stochastic non-triangular structure nonlinear systems is discussed in this article. Combined with the fixed-time stability theory, DSC technique and event-triggered control (ETC) technique, a novel event-triggered fixed-time adaptive controller is designed, under which both the closed-loop stability and the tracking performance can be guaranteed simultaneously in a fixed time. At the same time, the problems of “explosion of complexity” and “singularity” under the traditional backstepping design framework are avoided. Moreover, the design of event-triggered control mechanism can save the network resources effectively. In addition, the unknown nonlinear functions are approximated by some radial basis function neural networks (RBFNNs), and the filtering errors are compensated by the novel error compensating signals. Rigorous theoretical derivation and two simulations are included to illustrate the effectiveness of the proposed method.