【作者】 颜秉勇；

【导师】 田作华；

【作者基本信息】 上海交通大学 ， 控制理论与控制工程， 2010， 博士

【摘要】 随着现代控制系统的规模和复杂程度的逐渐增加,传感器、执行器及系统内部元器件都不可避免的会发生故障,因此对其安全性和可靠性的要求越来越高。故障诊断与容错控制技术是提高动态系统的安全性、可靠性的重要途径之一。因而深入研究故障诊断与容错控制技术,不但具有重要的理论意义,而且也具有巨大的实际应用价值。由于实际系统都是一定程度的非线性系统,加上系统建模误差和各种外部干扰的存在,使得对系统进行故障诊断变得更加复杂和困难。目前关于非线性系统的故障诊断技术受到广大学者的关注,是当前研究的热点和难点内容之一。本文针对非线性系统故障诊断技术领域内存在的一些问题及其发展趋势,结合相关学科理论的最新研究成果,研究了非线性系统故障诊断的若干方法及其应用。所取得的主要成果有:研究了一类非线性摄动时滞系统的鲁棒故障检测问题。分别采用了基于H∞/H?滤波器的方法和基于参考模型的方法设计了故障检测滤波器。通过分析所研究系统的特点,设计故障检测滤波器,利用鲁棒控制技术,将故障检测滤波器增益矩阵的求解问题转化为鲁棒稳定性分析问题,并给出了该问题解的存在的LMI条件和求解方法。所设计的故障检测滤波器,既考虑了故障检测结果对系统故障的敏感性,又考虑了对外界干扰的鲁棒性。研究了一类非线性不确定时滞系统的故障检测问题。首先根据系统的状态方程,建立反步观测器,并根据原系统的状态方程和反步观测器方程建立广义残差系统。然后,采用Backstepping设计方法,结合广义残差系统的特点,选取一个合适的李雅普诺夫函数,在证明故障检测系统稳定的同时,给出了反步观测器增益矩阵的存在条件和求解方法。结合预测控制中滚动优化的思想和迭代学习控制理论,设计了一种新型的故障跟踪估计器进行故障诊断。该方法是根据预测控制中滚动优化的思想,在一个优化时域内,根据系统实际运行时的输出和估计出的系统输出之差,通过一个迭代算法进行反复迭代运算来调节故障跟踪估计器中的虚拟故障,直到跟踪误差满足要求为止,此时的虚拟故障接近于实际故障,从而实现故障的检测与估计。文中给出了迭代学习算法的收敛性证明,并进行了故障跟踪特性分析。结果表明:迭代运算的初始条件,不影响最终故障估计的准确性;系统的不确定性(包含建模误差和外界扰动)会对故障估计带来误差。该方法不但适用于线性系统,也适用于非线性系统;既可用于确定性系统,也可用于不确定系统,适用面宽广。将自抗扰控制器中扩展状态观测器的思想用于故障诊断之中。根据扩展状态观测器的设计思想,将系统中发生的故障和不确定项看作系统的一个增广状态,通过对增广系统构建观测器进行状态估计(其中包括对增广状态的估计),来达到故障估计的目的。在非线性系统不确定部分范数有界的假设前提下,通过选取合适的阈值,可以有效的检测和估计非线性系统中的故障。同基于神经网络的故障诊断方法相比,该方法可以实时在线的进行故障检测和估计,大大提高了故障诊断的实时性。将该方法应用到了Vander Pol自激系统和机器手系统中。更多还原

【Abstract】 Modern systems are becoming more and more complex and sophisticated in their demand for performance, reliability and increasing autonomy. It is inevitable for sensors, actuators, and impotents inside the system that the fault occurs. Fault diagnosis and tolerant control technology is an important approach to improve the safety and reliability for dynamic systems. Research on fault diagnosis and tolerant control strategy has both theoretical and practical importance. However, the existence of the nonlinearity in the practical plant and uncertainty and noise of the plant models make it more and more difficult. At present, it has drawn wide attention, and has been one of the main topics in the control domain. In the thesis, according to the problems existed in the field of nonlinear fault diagnosis and the development trend of this subject, the fault diagnosis approaches for nonlinear systems and its applications are studied, and the main research results are given as follows:Robust fault detection approaches for a class of time-delay systems with nonlinear perturbations are studied. The design procedure of the fault detection filter is based on two methods: the H∞/ H? filter based approach and the reference model approach. By analyzing the characteristics of the system, design a fault detection filter, and then based on the robust control theory, the problem of designing the gain matrix of the fault detection filter can be solved by using the system’s robust stability analysis method. The existence and calculating methods of the gain matrix of the fault detection filter are also given in terms of LMI equality. The fault detection methods mentioned in this chapter,takes into account the sensitivity to system faults and robustness against system uncertainty simultaneously.Fault detection approach for a class of nonlinear time-delay systems with uncertainty is studied. First of all, according to the system equation, a Backstepping observer is constructed. For the purpose of fault detection, a general residual system is constructed by the system equation and residual system. Then, based on the Backstepping methods, a Lyapunov function is used to prove the stability of the Backstepping observer, and the solvable conditions of the gain matrix of the Backstepping observer are given at the same time.A novel fault tracking approximator (FTA) is proposed for fault diagnosis based on the predictive control and iterative learning control theory. Based on the predictive control theory, choose an optimization time span and adjust the virtual fault by using iterative learning algorithm according to the errors of the outputs of fault tracking approximator and the actual system outputs, until the errors meet the requirements. At this time, the virtual faults can approach the real system faults to diagnose the system faults. The convergence of this algorithm and the analysis of the tracking characteristics of the FTA are given in this paper. The results are given as follows: the initial conditions of the iterative learning algorithm do not have an effect on the accuracy of the fault tracking in the time axis. The system uncertainty, including the modelling errors and the noises, will bring fault tracking error. If the system uncertainty can be erased, the FTA can track the system faults。The fault tracking approximator can not only be used in linear systems, but in nonlinear systems; not only in general systems, but in uncertainty systems, and can be widely applied in real systems.The extended states observer (ESO) of the active disturbance rejection controller is used for fault diagnosis. According to the theory of ESO, the system faults and uncertainty are viewed as an extended system state. An fault diagnosis observer is constructed for the purpose of fault diagnosis and the estimation of the system states. Under the assumption that the uncertainty is bounded in terms of norm, the system faults can be detected by selecting appropriate threshold. Compared with the neural-network based fault diagnosis approach, the approach proposed in this chapter can detect and estimate the system fault in real time, which improve the efficiency of the fault diagnosis. Moreover, the approach is applied to Vander Pol oscillator system and robot arm system.更多还原