Fault Diagnosis And Fault Tolerant Control For Non-gaussian Time-delayed Stochastic Distribution Systems

To improve the reliability and safety of control systems, fault diagnosis (FD) and fault tolerance control (FTC) have long been regarded as the integrated and important part of the industrial process control.Fault diagnosis and fault tolerance control for stochastic systems is an important subject of the research, and it is always assumed that the disturbance, input or fault in the system obey a Gaussian-type distribution. However, this assumption is not wholly applicable to the actual application process. In addition, in many practical systems, the shape of the probability density function of the process control variables need to be controlled. The relationship between the system input and the output probability density function is described in such system equations, which is not the traditional relationship between the system input and output. Such system is called the stochastic distribution control system. In fact, time delay is an extremely common phenomenon in various industrial systems, which often exists in long pipeline to feed or belt transmission, very slow process or complicated online analyzers. The existence of time delay makes system analysis and synthesis more complex and difficult. Currently, there is no literature report about the fault tolerant control of the non-Gaussian time-delayed stochastic distribution control systems.In this thesis, for the non-Gaussian time-delayed stochastic distribution control system, fault is estimated using the effective fault diagnosis algorithm after the fault occurs. Based on the fault estimation information, fault tolerance control algorithm is presented to make the post-fault output probability density function (PDF) still track the given distribution, leading to the integrated fault diagnosis and fault tolerant control.In this thesis, the main contents are given as follows:①The rational square-root B-spline is used to approach the output probability density function. A fault diagnosis method based on the nonlinear adaptive observer is used to diagnose the constant fault of the stochastic distribution system with state delay. Finally computer simulations are given to demonstrate the effectiveness of the proposed algorithm.②To diagnose the fault in the dynamic part of the stochastic distribution system based on the rational square-root approximation, the novel design of a RBF neural network observer-based fault diagnosis algorithm is proposed. Based on the fault diagnosis information, design of new fault tolerant control scheme based on PI tracking control is given to make the post-fault probability density function still track the given distribution. Finally, simulation results are given to show the effectiveness of the integrated fault diagnosis and fault tolerant control scheme.③A new integrated fault diagnosis and fault-tolerant control strategy is proposed for the non-Gaussian singular time-delayed stochastic distribution control system. The fault diagnosis algorithm based on an iterative learning observer (ILO) is proposed. Combined with the switching control theory, the optimal fault tolerant control is carried out based on the fault estimation information to make the post-fault probability density function still track the given distribution. Finally, computer simulations are given to show the effectiveness of the integrated fault diagnosis and fault tolerant control algorithm, which is applicable not only to the constant fault, but also to the fast-varying fault and slow-varying fault.