Integrated Fault Diagnosis and Fault Tolerant Control Algorithm for Non-Gaussian Stochastic Distribution Systems

In this paper, an integrated fault diagnosis and fault tolerant control algorithm was proposed for a non-Gaussian nonlinear stochastic distribution control system. The RBF neural networks observer based fault diagnosis and PI tracking fault tolerant control were integrated to be designed. The rational square-root B-spline model was used to represent the output probability density function (PDF). On the basis of designed nonlinear neural network observer, a new fault diagnosis algorithm was developed to diagnose the slow-varying fault in the dynamic part of such systems. Convergency analysis was performed for the error dynamics raised from the fault detection and diagnosis phase. With the information of fault diagnosis, a new fault tolerant control scheme based on PI tracking strategy was designed so that the post-fault probability density function could still track the given distribution. A simulated example has been given to illustrate the efficiency of the proposed algorithms.