Fault tolerant control for non-Gaussian stochastic systems using RBF neural networks approximation model

In this paper, a new fault tolerant controller (FTC) algorithm for general stochastic nonlinear systems is studied. Different from the existing FTC methods, the measured information is the probability density functions (PDFs) of the system output rather than its value, where the radial basis functions (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings, so that the problem is transformed into a nonlinear FTC problem subject to the weight dynamical systems. The main objective of FTC requires detecting the occurrence of faults and maintaining the performance of the system in the presence of faults at a satisfying level. The FTC design consists of two steps. The first step is fault detection and diagnosis (FDD), which can produce an alarm when there is a fault in the system and also locate which component has a fault. The second step is to adapt the controller to the faulty case so that the system is able to achieve its target. A linear matrix inequality (LMI) based feasible FTC method is applied such that the fault can be detected and diagnosed. An illustrated example is included to demonstrate the use of control algorithm, and satisfactory results have been obtained.