Control And Fault Detection For Non-Gaussian Stochastic Distribution Systems

Traditional control techniques for stochastic systems are mainly concerned with mean and variance of the variable. In recent years, the stochastic control have formulated a new branch,which is called the stochastic distribute control, where the systems has its input as a deterministic variable and its output as the probability density function (PDF) of the systems output. The research for the stochastic distribution control (SDC) broke through stochastic variables subjected to the assumption of the Gaussian distribution, which is a new topic in stochastic control research, and formulated a relatively complete modeling and control theory.In the thesis, the control and fault detection and diagnosis for the non-Gaussian stochastic distribution systems will be described. In particular,The study of non-Gaus-sian stochastic distribution systems includes Tracking controll,Fault detection and diagnosis, Fault tolerant control. The theory of non-Gaussian stochastic distribution systems can been further supplement, improve through the article.the The main research work and contributions of this thesis are listed as follows:1.A guaranteed cost control for non-Gaussian stochastic distribution systems. Attention is focused on a memory state feedback control law based on linear matrix inequality (LMI) with model transformation such that the closed-loop systems is asymptotically stable and the guaranteed cost index is not more than a specified upper bound,which ensure that the systems output PDF follows the target PDF.2.Neuro-controller designing for guaranteed cost control of non-Gaussian uncertain stochastic distribution dontrol systems.neural controller for the guaranteed cost problem is vestigated. Based on LMI design,a class of a state feedback controller with a gain perturbations is structured,meanwhile,neural controller is used to tune the additive gain perturbations such that the guaranteed cost is reduced,and sufficient conditions for the existence of guaranteed cost controller are derived.3.Robust tracking control for the non-Gaussian stochastic distribution systems. We considers the design problem of a novel PID control based on Hoo for the non-Gaussian stochastic distribution systems.the PDF tracking control is transformed into a contrained tracking control problem for weight vector by B-spline expansion with modeling errors and the nonlinear weight model with exogenous disturbances. A design approaches PID control based on Hoo are provied to fulfil the PDF tracking problem. Based on LMI,the existence condition of Hoo controller is obtained. 4.Observer-based fault detection and diagnosis for non-Gaussian stochastic disribution systems.a new type of fault detection and diagnosis (FDD) problem for non-gausian stochastic distribution systems via the output PDFs) is investigated. The PDF can be approximated by using rational square-root B-spline expansion,via this expansions to represent the dynamics weighting systems between the systems input and the weights related to the output PDF. a nonlinear adaptive observer-based fault detection and diagnosis algorithm is present by introducing the adaptive tuning rule such that the residual is as sensitive as possible to the fault. Stability and convergency analysis is performed in fault detection and fault diagnosis analysis for the error dynamic systems.5.Fault detection and diagnosis for non-Gaussian singular stochastic distribution systems.The outputs of singular stochastic systems are described by PDF based on square root B-spline expansions. Then, non-linear observers are designed for the FDD. The conditions of stability of the correlative error estimation systemss are given by using LMI.6.Fault detection and diagnosis for non-Gaussian stochastic distribution systems with time delays. A RBF neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings. In this work, a nonlinear adaptive observer-based fault detection and diagnosis algorithm is presented by introducing the tuning parameter so that the residual is as sensitive as possible to the fault.7.Fault tolerant control for non-Gaussian stochastic distribution systems. Different from the conventional FTC methods, the measured information is the output PDFs rather than its instant values,where the RBFs neural network technique is introduced so that the output PDF can be formulated by the dynamic weights. Then based on Hoo techniques and PID controller, the concerned FTC problem can be transferred into a classical nonlinear FTC problem subject to a nonlinear systemss with both of modeling error and the fault. In terms of LMI techniques, a new control method is given so that the fault can be compensated or rejected.On the whole,this thesis investigates a series of new methods of tracking control and Fault detection and diagnosis for non-Gaussian stochastic distribution systems based on a variety of classical research methods in nonlinear control,stability analysis fields and Fault detection and diagnosis. It is noted that the above results have Important theoretical significance In research for the non-Gaussian stochastic distribution systems.