Adaptive Neural Network Control for Nonlinear Stochastic Switched Systems with Time-varying Full State Constraints

Li Zheng; Liu Lei; Liu Yan-jun

doi:10.12052/gdutxb.220040

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Adaptive Neural Network Control for Nonlinear Stochastic Switched Systems with Time-varying Full State Constraints

更新时间：2026-05-27

- Adaptive Neural Network Control for Nonlinear Stochastic Switched Systems with Time-varying Full State Constraints
- Vol. 39, Issue 5, Pages: 127-136(2022)
- 作者机构：
  
  辽宁工业大学理学院,辽宁,锦州,121001
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.220040
  CLC： TP273
- Published：2022
- 稿件说明：
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李争, 刘磊, 刘艳军. 具有时变全状态约束的非线性随机切换系统的自适应神经网络控制[J]. 广东工业大学学报, 2022, 39(5): 127-136.

Li Zheng, Liu Lei, Liu Yan-jun. Adaptive Neural Network Control for Nonlinear Stochastic Switched Systems with Time-varying Full State Constraints[J]. 2022, 39(5): 127-136.
李争, 刘磊, 刘艳军. 具有时变全状态约束的非线性随机切换系统的自适应神经网络控制[J]. 广东工业大学学报, 2022, 39(5): 127-136. DOI： 10.12052/gdutxb.220040.

Li Zheng, Liu Lei, Liu Yan-jun. Adaptive Neural Network Control for Nonlinear Stochastic Switched Systems with Time-varying Full State Constraints[J]. 2022, 39(5): 127-136. DOI： 10.12052/gdutxb.220040.

摘要

基于任意切换规则，以一类非线性不确定随机切换系统为研究对象，提出了一种具有时变全状态约束的自适应神经网络控制方案。在控制研究的过程中，采用神经网络对系统中的不确定项进行逼近处理。为了解决系统的约束问题，采用坐标变换技术，保证系统的所有状态均在约束界内，给出了闭环系统稳定性和收敛性的充分判据。最后的仿真实验表明所提出的控制策略能够达到较好的控制效果。本文所设计的控制策略大大提高了系统工作时的安全性。

Abstract

Based on arbitrary switching rules

an adaptive neural network control scheme with time-varying full state constraints is proposed for a class of nonlinear uncertain stochastic switching systems. In the process of control research

neural network is used to approximate the uncertain items in the system. In order to solve the constraint problem of the system

the coordinate transformation technology is used to ensure that all states of the system are within the constraint boundary

and the sufficient criteria for the stability and convergence of the closed-loop system are given. Finally

the simulation results show that the control strategy proposed in this research can achieve better control effect. The control strategy designed here can greatly improve the security of the system.

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Keywords

references

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