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

doi:10.12052/gdutxb.220040

Abstract

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.

Key words: stochastic switched, coordinate transformation, constrained control, nonlinear system

CLC Number:

TP273

Li Zheng, Liu Lei, Liu Yan-jun. Adaptive Neural Network Control for Nonlinear Stochastic Switched Systems with Time-varying Full State Constraints[J].Journal of Guangdong University of Technology, 2022, 39(05): 127-136.

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