Research On Robust Fault Detection And Estimation Methods For A Class Of Nonlinear Systems With Disturbances

With the rapid development of modern industrialization,the complexity and automation of industrial equipment and systems are increasing.How to improve the safety and reliability of the system and reduce the loss of property and casualties in the production process has become a major problem in the modern industrial system.Fault diagnosis technology is to improve the efficiency and reliability of the system by monitoring,discriminating,analyzing and making information in the process of equipment operation.Fault detection and estimation are very important in fault diagnosis.Therefore,fault detection and estimation have become a very important research topic in the field of automatic control.The fault diagnosis method based on linear model has obtained a series of research results,however,in the actual complex automatic control system,external disturbance and nonlinear dynamics are common,and the fault diagnosis of nonlinear system with disturbance has gradually become a hot and difficult problem.In this paper,the robust fault detection and estimation of nonlinear disturbance systems are studied.The main contents of the research include:(1)A robust fault detection and estimation observer design method for a class of nonlinear systems with disturbances is proposed.First,a robust fault detection observer is constructed and used as a residual generator to decouple this residual generator from the input.The H?performance index in the specified frequency domain is used to describe the sensitivity of the residual to the fault,and the robustness of the residual to the external interference of the system is described by using the H ? norm.The residual generator is sensitive to faults and is robust to unknown disturbances and noise.Thus,the influence of noise and disturbance on the residual error is suppressed and the fault detection is realized.Secondly,a robust fault estimation observer is designed to suppress the influence of unknown disturbances and system noise,but also to suppress that the fault estimation error is affected by the fault change rate,thus increasing the speed and robustness of the fault estimation.The residual signal adjustment after the fault estimation is suppressed by the H ? robust observer.Then the filter is designed to compare the size of the threshold with the norm residual to judge whether the filter is started.The design of the filter further enhances the system robustness.(2)Aiming at the problem that the iterative learning algorithm has a large estimation error and slow convergence speed in the process of nonlinear system fault detection and estimation.An adaptive iterative learning algorithm based on Runge-Kutta fault estimation observer model is proposed,which can effectively reduce the error of fault estimation;and the H ? performance index is introduced to improve the convergence rate of the fault estimation observer.The algorithm first designs the fault detection observer to detect the fault,then designs the fault estimation observer,and the adaptive algorithm is combined with the iterative learning strategy,so that the estimated fault gradually approaches the real fault,thus achieving accurate detection and estimation of many common faults in the nonlinear system.Finally,the effectiveness of the proposed algorithm is verified by the actuator fault simulation of the mechanically driven motor.(3)A fault estimation and isolation method based on iterative learning algorithm for generalized systems with disturbed Linear Parameter Varying is proposed.The proposed method designs q robust observer and fault estimation filter based on q actuator faults of the Linear Parameter Varying generalized multi-input multi-output system.First,assuming the jth actuator fault,the corresponding robust observer is designed using the method of H ?,which makes the residual signal robust to the disturbance and the residual fault.Secondly,the fault estimation wave is designed according to iterative learning algorithm.Then,the norm of the residual signal is compared with the threshold value of the designed filter to judge whether the fault estimation filter is started,thus realizing the fault estimation and isolation.