Research On Fault Diagnosis And Fault Tolerant Control Of Non-Gaussian Stochastic Distribution Systems

To improve the reliability and security of practical control systems, fault detection, diagnosis (FDD) and fault tolerant control (FTC) has long been regarded as an important and integrated part in control system design. It is well known that fault diagnosis and fault tolerant control for stochastic systems has been an important subject of research over the past decades. In these existing approaches, it has always been assumed that all the variables in the system obey a Gaussian-type distribution. However, for those systems with non-Gaussian signals, they couldn’t work very well. Motivated by those problems, a new kind of methods which directly control of the probability density functions. Therefore, the traditional stochastic system control and fault diagnosis method is not suitable for the non-Gaussian stochastic distribution control (SDC) systems.In this paper, fault detection and fault diagnosis algorithm is proposed for the non-Gaussian stochastic distribution control system. Based on the fault estimation information, fault tolerant control scheme is presented to make the post-fault PDF still track the given distribution. The main contributions of this thesis include the following issues:①Based on RBF neural networks observer fault diagnosis (FD) and PI tracking strategy fault tolerant control (FTC) algorithms are presented for non-Gaussian nonlinear stochastic distribution control (SDC) systems. In this paper, the rational square-root B-spline model is used to represent the dynamics between the output PDF and the input. This is then followed by the novel design of a nonlinear neural network observer-based fault diagnosis algorithm so as to diagnose the fault in the dynamic part of such systems. Convergence analysis is performed for the error dynamics raised from the fault detection and diagnosis phase. Finally, based on the fault diagnosis information, a new fault tolerant control based on PI tracking strategy control scheme is designed to make the post-fault probability density function still track the given distribution. A simulated example is given to illustrate the efficiency of the proposed algorithms. ②New fault diagnosis (FD) and fault tolerant control (FTC) algorithms for non-Gaussian singular stochastic distribution control (SDC) systems are presented in the paper. Different from general SDC systems, in singular SDC systems, the relationship between the weights and the control input is expressed by a singular state space mode, which increases the difficulty in the FD and FTC design. The proposed approach relies on an iterative learning observer (ILO) for fault estimation. Based on the estimated fault information, the fault tolerant controller can be designed to make the post-fault probability density function (PDF) still track the given distribution. Computer simulations are given to show the effectiveness of the proposed FD and FTC algorithms whether the abrupt fault, the slow-varying fault or the fast-varying fault occurs.③In this paper a robust Iterative Learning Control (ILC) based fault diagnosis (FD) and fault tolerant control (FTC) algorithms is proposed for the shape control of the output probability density functions (PDF) for dynamic stochastic systems subjected to Non-Gaussian variables. By separating the whole control horizon into certain number of the time domain sub-intervals called Batches, a fault diagnosis and control algorithm is established where the Youla parametrization technique has been used together with a proportional plus differential (PD) version of the ILC. The proportional part of the ILC law looks after the tuning of the RBFNN basis function parameters (i.e., the RBF centers and widths) whilst the differential part of the ILC law is used to tune the parameters of Youla parameterized controller so that the closed-loop output PDF tracking performance is improved versus the advances of batches along the time horizon. The convergence analysis of the proposed ILC is also made.