Adaptive Fixed-Time NN-Based Tracking Control for a Type of Stochastic Nonlinear Systems Subject to Input Saturation

Daohong Zhu, Zhenzhen Long and Liandi Fang ()
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Daohong Zhu: College of Mathematics and Computer Science, Tongling University, Tongling 244061, China
Zhenzhen Long: School of Mathematics and Statistics, Anhui Normal University, Wuhu 241005, China
Liandi Fang: College of Mathematics and Computer Science, Tongling University, Tongling 244061, China

Mathematics, 2025, vol. 13, issue 12, 1-20

Abstract: This paper considers the adaptive fixed-time tracking control problem for stochastic systems subject to input saturation. Firstly, a smooth function approximation method is utilized to eliminate the effect of input saturation. Then, by combining the neural networks (NNs) approximation method with the backstepping-like technique, an adaptive fixed-time tracking control scheme is explicitly developed. The proposed scheme can ensure that the state variables are bounded in probability and the tracking error converges to a small region of the equilibrium point in a fixed time. Eventually, two numerical examples are given to indicate the performance and effectiveness of the presented strategy.

Keywords: fixed-time tracking control; stochastic systems; input saturation; neural network; backstepping-like technique (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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