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

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 th...

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Main Authors: Daohong Zhu, Zhenzhen Long, Liandi Fang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/13/12/2018
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author Daohong Zhu
Zhenzhen Long
Liandi Fang
author_facet Daohong Zhu
Zhenzhen Long
Liandi Fang
author_sort Daohong Zhu
collection DOAJ
description 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.
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issn 2227-7390
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spelling doaj-art-d1caf884a2c14888bf3e7c5cadd17f402025-08-20T03:16:22ZengMDPI AGMathematics2227-73902025-06-011312201810.3390/math13122018Adaptive Fixed-Time NN-Based Tracking Control for a Type of Stochastic Nonlinear Systems Subject to Input SaturationDaohong Zhu0Zhenzhen Long1Liandi Fang2College of Mathematics and Computer Science, Tongling University, Tongling 244061, ChinaSchool of Mathematics and Statistics, Anhui Normal University, Wuhu 241005, ChinaCollege of Mathematics and Computer Science, Tongling University, Tongling 244061, ChinaThis 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.https://www.mdpi.com/2227-7390/13/12/2018fixed-time tracking controlstochastic systemsinput saturationneural networkbackstepping-like technique
spellingShingle Daohong Zhu
Zhenzhen Long
Liandi Fang
Adaptive Fixed-Time NN-Based Tracking Control for a Type of Stochastic Nonlinear Systems Subject to Input Saturation
Mathematics
fixed-time tracking control
stochastic systems
input saturation
neural network
backstepping-like technique
title Adaptive Fixed-Time NN-Based Tracking Control for a Type of Stochastic Nonlinear Systems Subject to Input Saturation
title_full Adaptive Fixed-Time NN-Based Tracking Control for a Type of Stochastic Nonlinear Systems Subject to Input Saturation
title_fullStr Adaptive Fixed-Time NN-Based Tracking Control for a Type of Stochastic Nonlinear Systems Subject to Input Saturation
title_full_unstemmed Adaptive Fixed-Time NN-Based Tracking Control for a Type of Stochastic Nonlinear Systems Subject to Input Saturation
title_short Adaptive Fixed-Time NN-Based Tracking Control for a Type of Stochastic Nonlinear Systems Subject to Input Saturation
title_sort adaptive fixed time nn based tracking control for a type of stochastic nonlinear systems subject to input saturation
topic fixed-time tracking control
stochastic systems
input saturation
neural network
backstepping-like technique
url https://www.mdpi.com/2227-7390/13/12/2018
work_keys_str_mv AT daohongzhu adaptivefixedtimennbasedtrackingcontrolforatypeofstochasticnonlinearsystemssubjecttoinputsaturation
AT zhenzhenlong adaptivefixedtimennbasedtrackingcontrolforatypeofstochasticnonlinearsystemssubjecttoinputsaturation
AT liandifang adaptivefixedtimennbasedtrackingcontrolforatypeofstochasticnonlinearsystemssubjecttoinputsaturation