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

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

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。

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 closedloop 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.