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

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. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This article discusses the problem of event-triggered fixed-time adaptive neural dynamic surface control for stochastic non-triangular structure nonlinear systems. A novel event-triggered fixed-time adaptive controller is designed to ensure both closed-loop stability and tracking performance simultaneously in a fixed time, while avoiding the issues of complexity explosion and singularity under the traditional backstepping design framework. The design of event-triggered control mechanism effectively saves network resources and the effectiveness of the proposed method is demonstrated through rigorous theoretical derivation and simulations.