基于迭代学习观测器的随机分布系统容错控制

Fault-Tolerant Control for Stochastic Distribution Systems Based on Iterative Learning Observer

针对包含故障和干扰的非高斯随机分布系统,提出了一种基于迭代学习观测器(ILO)的鲁棒容错控制方法。使用线性B样条神经网络建立了输出概率密度函数(PDF)和动态权重之间的关系。设计迭代学习观测器,以较小的计算量实现对故障的精确估计。利用故障估计信息设计容错控制器,使得系统的权向量在故障发生后仍能够跟踪到期望的权向量。最后通过仿真分析说明了提出方法的有效性。所设计的迭代学习观测器在经过短暂过渡后可迅速重构系统故障,基于PI跟踪的容错控制器对定常和时变权向量都有较好的跟踪效果。

For non-Gaussian stochastic distribution systems with faults and disturbances,a novel robust fault-tolerant control scheme is proposed based on Iterative Learning Observer(ILO).The linear B-spline neural network is used to establish the relationship between output Probability Density Function(PDF) and dynamic weights.An iterative learning observer is designed to accurately estimate the faults with less computational load.By using the fault estimation information,a fault-tolerant controller is designed,so that the system can still track the expected weight vector after the fault occurs.Simulation results demonstrate the effectiveness of the proposed approach.The iterative learning observer can rapidly reconstruct the faults after a short transition period,and the fault-tolerant controller based on PI tracking has good tracking effects on both the constant and time-varying weight vectors.