一类随机时延网络控制系统的神经网络自适应容错控制

ADAPTIVE FAULT-TOLERANT CONTROL FOR A CLASS OF NETWORKED CONTROL SYSTEM WITH RANDOM TIME DELAY BASED ON NEURAL NETWORK

针对一类随机时延网络控制系统,提出一种基于RBF神经网络自适应动态补偿的容错控制策略.该方法通过在线估计时延将系统建模为随机切换系统,并在模型参考自适应方法的基础上设计RBF神经网络动态补偿容错控制器,利用Lyapunov稳定性理论给出神经网络补偿器的在线权值学习算法,以保证网络控制系统在故障情况下的跟踪性能和状态一致最终有界稳定.最后通过仿真验证了该方法的有效性.

A novel fault tolerant control strategy based on radial basis function （RBF） neural network adaptive compensation is presented for a class of networked control system with random time delay. The networked control system is modeled as stochastic switched system by estimating time delay online and an RBF neural network is designed to compensate effect of the system faults and disturbances dynamically by using model reference adaptive control technology. Furthermore the weights tuning law of neural network is given through Lyapunov stability theory to ensure the tracking performance and system state uniformly ultimately bounded when failures occur. Finally, the simulation results are given to illustrate the feasibility of the proposed method.