Observer-Based Fixed-Time Neural Control for a Class of Nonlinear Systems

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 7; pp. 1 - 11
• Main Authors: Zhang, Yan, Wang, Fang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive neural control; Artificial neural networks; Asymptotic stability; Control systems; Control systems design; fixed-time control; Hysteresis; Mathematical model; Neural networks; Nonlinear systems; nonlinear uncertain systems; Nonlinearity; Stability criteria; state observer; State observers
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.3046865

This article is concerned with an issue of fixed time adaptive neural control for a class of uncertain nonlinear systems subject to hysteresis input and immeasurable states. The state observer and neural networks (NNs) are used to estimate the immeasurable states and approximate the unknown nonlinearities, respectively. On this foundation, an adaptive fixed time neural control strategy is developed. Technically, this control strategy is based on a novel fixed-time stability criterion. Different from the research on fixed-time control in the conventional literature, this article designs a new controller with two fractional exponential powers. In the light of the established stability criterion, the fixed-time stability of the systems is guaranteed under the proposed control scheme. Finally, a simulation study is carried out to test the performance of the developed control strategy.