基于自适应观测器的时滞系统执行器故障诊断

该文研究了一类含有未知输人干扰和模型不确定性的线性时滞系统的故障诊断问题。通过设计自适应诊断观测器，得到了一种新型的鲁棒执行器故障诊断方法。首先针对确定性系统分别设计了检测观测器和自适应诊断观测器，前者能够检测出故障的发生，后者能够理想地估计出故障随时间变化的形状。然后考虑系统的外部干扰和模型不确定，改进了自适应诊断观测器的算法，证明了故障诊断系统的稳定性，提高了故障诊断系统的鲁棒性。最后给出了故障检测过程中阈值的选取原则。仿真结果表明算法具有良好的诊断性能。     

基于自适应奇异观测器的连续系统故障诊断

针对连续系统的传感器与执行器故障诊断问题,提出一种自适应奇异观测器来进行故障估计,能同时估计系统中的传感器故障与执行器故障,并克服了以往研究中对故障、故障导数与系统干扰作上界已知假设的不足;利用H∞性能指标抑制了干扰对故障估计的影响;通过线性矩阵不等式来获得自适应奇异观测器的增益阵,从而很方便地完成故障诊断观测器的设计;采用Lyapunov泛函证明了观测误差动态系统是鲁棒渐近稳定的;最后通过仿真实例验证了所提方法的有效性。     

双电机同步驱动伺服系统故障诊断与容错控制

本文针对双电机同步驱动伺服系统中执行器失效会导致系统性能下降甚至失稳的情况,提出了一种基于自适应滑模的故障诊断和容错控制策略.该方法通过设计各电机转速的自适应滑模状态观测器,在线估计各执行器的失效因子:当单个执行器部分失效时,通过自适应的方法调整控制器增益;当单个执行器全部失效时,重构系统的控制律.对于系统中存在非匹配不确定项的情况,提出在期望虚拟信号中引入基于扩张状态观测器的补偿项抑制方案;利用Lyapunov理论证明了闭环系统在正常和故障状况下的稳定性以及观测器的收敛性;仿真结果表明,所设计的控制策略能保证系统稳定跟踪指令信号,在单个执行器失效的情况下系统跟踪性能基本不下降.     

控制输入饱和的柔性机械手容错控制

针对挠性机械手易受到建模误差和多源干扰等影响，该文提出了一种基于观测器的自适应容错控制策略。首先，构造干扰观测器（DO）和故障诊断观测器（FDO），分别对机械手的挠性附件产生的振动和执行器故障进行实时估计和抵消；其次，考虑了物理结构限制会导致执行器输入饱和情况，引入抗饱和算法，不仅降低了控制器设计难度，通过约束函数也保证了系统广义状态的稳定性；最后，通过MATLAB/Simulink仿真进一步验证了所提出方法的有效性。     

基于参数估计的一类非线性系统故障诊断算法

针对系统模型的不确定性、未知输入扰动和非线性特性, 提出一类非线性系统参数估计的故障诊断算法. 构造系统故障诊断观测器, 采用Lyapunov 稳定性定理验证观测器的稳定性, 通过Barbalat 引理证明满足故障诊断观测器为渐近稳定的表征故障参数的参数估计, 并总结了设计算法流程. 仿真结果表明, 所提出算法具有快速收敛性, 对一类非线性系统诊断效果较好.

     

一类非线性系统的自适应观测器设计

在故障诊断应用中, 状态方程中的未知参数和输出方程中的未知参数分别表征执行机构故障和传感器故障, 所以研究状态方程和输出方程同时含有未知参数的自适应观测器有着实际的应用意义. 本文基于高增益观测器和自适应估计理论, 针对状态方程和输出方程同时含有未知参数的一类一致可观的非线性系统, 用构造性方法设计了一种联合估计状态和未知参数的自适应观测器. 该自适应观测器的参数估计采用时变增益矩阵, 结构形式及参数设置简单. 给出了使该自适应观测器满足全局指数收敛性的持续激励条件, 并在理论上简洁地证明了该自适应观测器的全局指数收敛性. 数值仿真结果表明该自适应观测器具有良好的快速收敛性、跟踪性等期望性能.     

基于观测器的机械臂执行器故障诊断方法研究

为了提高机器人系统的可靠性，针对机器人的机械臂控制系统，提出一种基于观测器的执行器故障诊断方法。在考虑系统存在外部干扰的情况下，通过设计干扰观测器抵消外部干扰对故障诊断结果的影响。为了进一步诊断系统执行器故障，提出一种基于二阶滑模观测器的故障诊断方法，通过等效输出注入项对系统执行器故障信号进行故障估计，以完成机械臂执行器的故障诊断任务。最后，建立了双关节机械臂控制系统仿真模型并通过数值仿真分析说明了该故障诊断方法的有效性和应用前景。因此，本研究有助于提高工业机器人系统的可靠性和安全性。     

一种容错飞行控制的神经网络方法研究

文章提出了一种新的主动容错飞行控制系统设计方法,可同时进行飞控系统执行器的故障诊断和容错控制;首先建立飞机执行器故障模型,接着应用改进的BP神经网络算法,进行飞行控制系统模型辨识,实时进行故障诊断;然后根据故障诊断信息进行自适应容错控制,为了克服故障系统引起的模型误差和非线性因素的影响,设计了自适应神经网络PID参数整定和动态逆控制器,对飞行控制系统执行器故障进行容错控制,以实现系统的良好模型跟踪和动态性能;仿真结果表明,在保证闭环系统稳定的前提下,实现了执行器的在线故障诊断与容错控制,达到了理想的效果.     

基于自适应未知输入观测器的非线性动态系统故障诊断

针对以往故障诊断研究中要求故障或故障导数及系统干扰的上界是已知的不足,以及难以同时诊断执行器故障和传感器故障的问题,提出一种自适应未知输入故障诊断观测器,能够同时重构非线性动态系统的执行器故障和传感器故障.首先,利用H_∞性能指标抑制未知输入对故障重构的影响,采用Lyapunov泛函得到观测误差动态系统的稳定性;然后,通过线性矩阵不等式求解观测器增益阵,并实现故障重构;最后,通过直流电机系统的仿真验证了所提出方法的有效性.     

基于未知输入观测器的非线性广义系统执行器故障检测和重构

针对一类含有未知扰动广义非线性系统的执行器故障,本文提出一种重构算法。首先设计未知输入观测器对干扰鲁棒,作为故障检测观测器。检测到发生故障后,通过提出含有误差比例项和积分项的故障估计算法,形成自适应观测器,实现准确快速地估计故障,同时估计状态变量。根据李雅普诺夫稳定理论给出估计误差一致最终有界的充分条件。最后仿真验证该类观测器和重构算法的有效性。     

Nonlinearities enhance parameter convergence in output-feedbacksystems

While the parameter convergence properties of standard adaptive algorithms for linear systems are well established, there are no similar results on the parameter convergence of adaptive controllers for nonlinear systems which have gained popularity in recent years. In this paper we focus on a recently developed class of adaptive schemes for output-feedback nonlinear systems and show that parameter convergence is guaranteed if and only if an appropriately defined signal vector, which does not depend on closed-loop signals, is persistently exciting. Then we develop an analytic procedure which allows us, given a specific nonlinear system and a specific reference signal, to determine a priori whether or not this vector is persistently exciting (PE) and, hence, whether or not the parameter estimates will converge. In the process we show that the presence of nonlinearities usually reduces the sufficient richness (SR) requirements on the reference signals and hence enhances parameter convergence. This is the first result on the relationship between PE and SR for adaptive nonlinear control systems     

一类非线性系统的自适应控制方法1)

非线性系统的模型参考自适应控制是自适应理论的一个新的发展方向,目前针对可反馈线性化的系统已经取得了很多研究成果.但以往采用的方法要求系统对未知参数是线性的,且计算复杂度随系统阶次或相对阶的升高而升高.给出一种新的非线性模型参考自适应跟踪控制方法,证明了无需未知参数以线性形式存在,而只要求回归向量对参数满足一定的Lipschitz条件即可保证系统具有期望的特性.     

一类非线性系统的自适应控制方法

非线性系统的模型参考自适应控制是自适应理论的一个新的发展方向,目前针对可 反馈线性化的系统已经取得了很多研究成果.但以往采用的方法要求系统对未知参数是线性 的,且计算复杂度随系统阶次或相对阶的升高而升高.给出一种新的非线性模型参考自适应 跟踪控制方法,证明了无需未知参数以线性形式存在,而只要求回归向量对参数满足一定的 Lipschitz条件即可保证系统具有期望的特性.     

Adapting the search vector for direct adaptive control systems

In adaptive control algorithms, the adaptation routine (e.g., least squares or gradient) is usually used to adjust the controller parameters to approximate the ideal controller that is assumed to exist. Searching for the ideal parameter vector, a gradient-based hybrid adaptive routine is used here for continuous-time nonlinear systems. The adjustment of the parameter vector is usually based on minimizing the squared error. For direct adaptive control, in this paper an algorithm is presented to adapt the direction of the search vector so that the instantaneous control energy is minimized. Hence, the overall adaptive routine minimizes not only the squared error but also the instantaneous control energy. Stability results of the presented algorithm show that boundedness of the error is dependent on the length of the search vector.     

Nonlinear adaptive control system design with asymptotically stable parameter estimation error

The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.     

GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems

In this paper, the stability analysis of the GA-based adaptive fuzzy sliding model controller for a nonlinear system is presented. First, an uncertain and nonlinear plant for the tracking of a reference trajectory is well approximated and described via the reference model and the fuzzy model involving fuzzy logic control rules. Next, the difficulty in designing a fuzzy sliding mode controller (FSMC) capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. The initial values of the consequent parameter vector are decided via the genetic algorithm. After this, a modified adaptive law can be adopted to find the best high-performance parameters for the fuzzy sliding model controller. The adaptive fuzzy sliding model controller is derived to simultaneously stabilize and control the system. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov’s direct method. Finally, a numerical simulation is provided as an example to demonstrate the control methodology.     

Improved transient performance of nonlinear adaptive backsteppingusing estimator resetting based on multiple models

It is proved that the the transient performance of nonlinear adaptive backstepping can be improved by re-setting the parameter estimator, without loss of stability. The estimator re-setting algorithm is based on multiple-model adaptive control, where a number of models with fixed parameter vectors are monitored online in order to detect a parameter vector that gives a negative jump in the control Lyapunov function when replacing the estimate provided by the standard adaptation law. An application to wheel slip control is studied     

Parameter convergence and minimal internal model with an adaptive output regulation problem

The parameter convergence of nonlinear adaptive control systems is an important yet not well addressed issue. In this paper, using the global robust output regulation problem of output feedback systems with unknown exosystems as a platform, we will show that the well known persistency of excitation (PE) condition still guarantees the convergence of the estimated parameter vector to the true parameter vector. Moreover, the PE condition will be satisfied if the internal model is minimal in certain sense.     

Adaptive Nonlinear Actuator Fault Diagnosis for Uncertain Nonlinear Systems with Time‐Varying Delays

An actuator fault diagnosis method is presented in this paper for a class of time‐delayed nonlinear systems via the use of adaptive updating rules. The considered system is represented by a dynamic state space model where the time delays are embedded into the state vector. Model parameters are not perfectly known, which lead us to consider norm bounded uncertainties. An adaptive fault diagnosis observer is designed where the Lyapunov stability theory is used to obtain the required adaptive tuning rule for estimation of the nonlinear actuator fault. A simulated numerical example is included to demonstrate the effectiveness of the proposed approach.     

基于观测器的不确定非线性系统的自适应鲁棒模糊控制

针对单输入单输出不确定非线性系统提出了一种自适应鲁棒模糊控制算法.该算法通过设计观测器来估计系统的状态向量,因此不要求假设系统的状态向量是可测的.在这个算法中,主要的假设为最优逼近参数向量与标称参数向量之差的范数和逼近误差的界限是未知的.通过只对未知界限估计的调节,该算法减轻了在线计算量并且提高了系统的鲁棒性.所设计的自适应鲁棒模糊控制算法保证了闭环系统的所有信号是一致有界的并且跟踪误差估计收敛到一个小的零邻域内.仿真例子证实了所提方法的可行性.