Research On Fault Detection And Control Of Uncertain Nonlinear Systems

With the rapid development of science and technology and productivity,nonlinear systems are widely found in various industries in production and life.In particular,modern industrial control systems are increasingly complex and highly non-linear,which imposes higher requirements on the reliable operation of the system.System fault will lead to weakened control performance of the system,causing equipment failures,and serious problems.Therefore,it has important practical significance to study the fault detection and control of nonlinear systems..This paper focuses on the research of fault detection and control for nonlinear systems.First,this paper introduces the theoretical basis of the fault detection and control of the nonlinear system,and introduces some technical methods used in this paper,such as several common theorems and the principles of neural network.Secondly,for a class of uncertain nonlinear systems with unmodeled dynamics,the unknown state is estimated by designing a K-filter to obtain the error signal needed for fault detection and observer-based fault detection.Based on this,an adaptive neural network is used to approximate the fault function,and the unmodeled dynamics are dealt with by changing the energy supply method.Based on the idea of back-stepping design,an adaptive output feedback fault detection and control scheme is proposed.System faults are accurately detected and ensure that the system operates stably before and after a fault occurs,making all states bounded.Finally,a decentralized K filter is applied to estimate the unknown state for a class of uncertain interconnected nonlinear systems with measurement noise,so as to get the bias signals needed for fault detection,so as to realize the decentralized fault detection based on the observer and filter methods.The filtering method is used to eliminate the bad influence of the system output with measurement noise on the fault detection.Based on the design of decentralized filter observer and back-stepping design method,using neural network approximation and adaptive techniques,we propose a decentralized adaptive output feedback fault detection and control strategy that can ensure accurate detection after a fault occurs.And to ensure that the output of each subsystem accurately track the given signal before and after the occurrence of the fault,to ensure tracking error and all states are bounded.