Fault diagnosis and fault tolerant control for the non-Gaussian time-delayed stochastic distribution control system

The main feature of the stochastic distribution control system is the output probability density function rather than the real value. The effectiveness of the fault detection, diagnosis and fault tolerant control will be reduced when time delay exists in control systems. In this paper, the rational square-root B-spline is used to approach the output probability density function. In order to diagnose the fault in the dynamic part of such systems, it is then followed by the novel design of a nonlinear neural network observer-based fault diagnosis algorithm. Based on the fault diagnosis information, a new fault tolerant control based on PI tracking control scheme is designed to make the post-fault probability density function still track the given distribution. Finally, simulations for the particle distribution control problem are given to show the effectiveness of the proposed approach.