基于迭代学习的离散线性时变系统故障诊断

针对一类离散线性时变系统的故障诊断问题,提出一种新的故障检测与估计算法.该算法通过引入虚拟故障构建离散故障跟踪估计器,在选取的优化时域内,利用估计器输出和系统实际输出产生的残差信号,采用迭代学习算法来调节虚拟故障,使虚拟故障逼近系统中实际发生的故障,从而达到对系统故障诊断的目的.该方法不仅能检测出系统不同类型的故障,还可以实现对故障信号的精确估计.仿真结果验证了所提出方法的有效性.     

基于未知输入观测器的直流电机鲁棒故障估计

针对直流电机系统提出一种基于未知输入观测器(UIO)的鲁棒故障估计方法,同时估计系统中的执行器故障和传感器故障。首先,构建包含系统传感器故障的增广状态系统;然后,基于该增广系统提出一种新颖的UIO,并给出了该观测器的存在条件和多故障估计策略;同时,引入H_∞性能指标最大程度地抑制干扰对故障估计结果的影响;接着,给出观测器的设计条件和参数求解过程并将其转化为易于求解的线性矩阵不等式(LMI)的形式;最后,通过算例仿真和实验验证了该方法的有效性和可用性。     

基于自适应迭代学习算法的一类非线性系统故障检测与估计

针对迭代学习算法在非线性系统故障检测与估计过程中存在估计误差较大和收敛速度较慢等不足的问题,提出了一种基于龙格–库塔故障估计观测器模型的自适应迭代学习算法,有效降低了故障估计误差;并引入H∞性能指标,提高了故障估计观测器的收敛速度.该算法首先设计故障检测观测器对故障进行检测,然后设计故障估计观测器,并将自适应算法与迭代学习策略相结合,使得估计故障逐渐逼近真实故障,从而实现对非线性系统中多种常见故障的精确检测与估计.最后,通过机械臂旋转关节驱动电机的执行器故障仿真验证了所提算法的有效性.     

一类受扰线性采样数据系统的鲁棒执行器故障估计

针对一类含有外部干扰的线性采样数据系统, 本文研究了执行器故障估计问题. 首先, 文章设计了一种用于估计系统执行器故障的鲁棒故障估计观测器, 在连续采样时间间隔内, 观测器增益矩阵指数变化. 之后, 通过联合增广状态估计误差与故障误差, 并利用Lyapunov-Krasovskii泛函和线性矩阵不等式技术, 给出了保证误差系统全局渐近稳定的充分条件. 同时, 建立的线性矩阵不等式条件涉及调谐参数和最大采样间隔, 可以较好改善状态和故障估计性能. 最后, 通过对某型民航飞机模型实例进行仿真, 验证了本文所提方法具有更好的跟踪效果     

多智能体系统的鲁棒故障估计观测器的设计

针对线性变参数多智能体系统设计了有限频域鲁棒故障估计观测器。首先,根据每个智能体的绝对可测输出和相对可测输出建立了每个节点的动力学方程,结合无向通讯拓扑图及拉普拉斯矩阵得到了多智能体系统的动力学方程,通过合适的变换对多智能体系统模型进行了解耦;然后,根据解耦后的系统动力学方程设计了故障估计观测器,并通过优化技术得到了故障估计观测器增益矩阵和优良的鲁棒性能指标;最后,通过微型飞行器纵向飞行运动的例子验证了所设计的故障估计观测器的有效性,及系统参数在一定的范围内发生变动的时候,故障估计观测器依然可以准确的估计系统所发生的故障。     

基于ESO迭代学习算法的故障估计

针对基于迭代学习的故障估计器方法,提出一种基于扩张状态观测器(ESO)思想的迭代学习算法,以提高虚拟故障的收敛速度。该算法将ESO的输出误差非线性反馈机制用于迭代学习过程,利用故障估计器当前输出残差的非线性函数修正下次迭代时的虚拟故障值。对所建立的故障估计器的收敛性进行理论分析,并在此基础上进行了仿真实验。仿真结果表明,所提出的算法具有良好的收敛速度和故障估计精度。     

一类不确定线性系统的鲁棒故障观测器设计

研究线性时不变系统的鲁棒故障观测器设计问题,给定故障检测率,降低误报率作为优化指标,对故障和未知扰动在残差信号中的输出进行滤波。再考虑不确定性影响,运用模型匹配方法,提出了具有不确定性线性系统的鲁棒故障观测器设计方法,并以线性矩阵不等式形式给出问题求解算法。通过求解具有线性矩阵不等式约束的凸优化问题获得观测器的全局最优解。通过仿真表明,改进的设计方法可有效保障残差信号对故障信号的灵敏度,并改善了诊断性能。     

一种新的基于自适应观测器中立时滞系统故障估计的设计方法

研究了基于自适应观测器中立时滞系统的故障估计问题. 首先, 本文提出了一种新的快速自适应故障估计算法提升了故障估计的快速性和准确性. 同时, 一个时滞相关的判据用于减少设计过程中的保守性, 特别对于小时滞系统. 然后, 应用线性矩阵不等式技巧, 给出了详细的设计步骤. 最后, 仿真结果验证了所提方法的有效性.     

基于自适应滑模观测器的不匹配非线性系统执行器故障重构

针对一类含有未知干扰的不匹配非线性Lipschitz系统,提出了基于自适应滑模观测器的执行器故障重构方法.首先引入辅助输出矩阵,使得辅助输出系统的观测器匹配条件得以满足,同时设计了高增益观测器实现对未知辅助输出的精确估计;然后针对辅助输出系统建立故障重构滑模观测器,设计了自适应律在线修正滑模控制器增益,考虑故障上界未知的前提下,提出了观测器状态估计误差稳定的存在定理,运用Schur补引理将观测器反馈增益矩阵设计方法转化为求解线性矩阵不等式约束优化问题,同时引入线性变换矩阵,在故障上界未知的前提下设计了滑模控制增益,使得输出估计误差收敛稳定,确保了滑模运动在有限时间内发生,在此基础上利用等效控制输出误差注入原理实现了执行器故障重构;最后通过仿真算例验证了本文方法的有效性.     

非线性系统的主动容错控制

对一般的非线性系统提出了一种新的主动容错控制方法.系统正常工作时,采用基于迭代学习观测器的输出反馈控制策略,控制器为迭代学习观测器的状态和调节参数的函数,此输出反馈控制器能良好地镇定该非线性系统.当系统发生故障后,进行控制器重组,在调节参数的自适应调节律中引入了故障估计的信息,使得系统发生故障后包含故障估计信息的重组控制器仍然能使系统稳定,实现了非线性系统的主动容错控制.计算机模拟显示所提出算法的有效性.     

Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.     

基于泰勒级数的迭代学习算法

针对存在不确定扰动的线性时变系统的轨迹跟踪控制问题，提出了基于泰勒级数的迭代学习算法．该算法利用泰勒级数将系统参数化，导出一种基于泰勒级数的线性时变系统的近似模型．在此模型的基础上，利用迭代学习方式修正输入量的泰勒展开系数，并用LMI方法求解学习增益矩阵．所提出算法在系统不满足正则性或无源性时，仍可用输出误差信号来构造学习律．仿真结果表明了该算法的有效性．     

基于二维系统的迭代学习事件触发鲁棒控制

本文研究了二维系统框架下,带有事件触发机制的不确定离散系统迭代学习鲁棒控制问题.首先为了减少迭代过程中控制信号的更新次数,构建了一种沿迭代轴的事件触发机制,并提出了基于事件触发机制的迭代学习控制算法.基于二维系统理论,将迭代学习过程转化为等价二维Roesser系统.构造李雅普诺夫函数,结合线性矩阵不等式(LMI)技术,给出了系统渐近稳定的充分条件,进一步得到了控制器增益的求取方法.最后仿真结果验证了提出的事件触发机制的有效性.     

一类不确定性离散非线性重复过程的迭代学习容错控制

针对一类具有非线性和执行器故障的重复运行不确定离散系统,提出了一种迭代学习鲁棒容错控制算法.首先通过定义执行器故障系数矩阵,将迭代学习控制过程转化为等价形式的不确定性非线性重复过程模型,然后基于混合李亚普若夫函数方法讨论非线性重复过程在时间轴和批次轴两个维度上的稳定性,并以线性矩阵不等式形式给出鲁棒容错控制器存在的充分条件和设计方法,同时保证系统正常和执行器故障情形下系统的容错稳定性能.最后,单杆机械手系统的输出跟踪控制仿真结果验证了本文算法的有效性.     

Observer‐based Fault Estimators Using Iterative Learning Scheme for Linear Time‐delay Systems with Intermittent Faults

This paper deals with the fault estimation problem for a class of linear time‐delay systems with intermittent fault and measurement noise. Different from existing observer‐based fault estimation schemes, in the proposed design, an iterative learning observer is constructed by using the integrated errors composed of state predictive error and tracking error in the previous iteration. First of all, Lyapunov function including the information of time delay is proposed to guarantee the convergence of system output. Subsequently, a novel fault estimation law based on iterative learning scheme is presented to estimate the size and shape of various fault signals. Upon system output convergence analysis, we proposed an optimal function to select appropriate learning gain matrixes such that tracking error converges to zero, simultaneously to ensure the robustness of the proposed iterative learning observer which is influenced by measurement noise. Note that, an improved sufficient condition for the existence of such an estimator is established in terms of the linear matrix inequality (LMI) by the Schur complements and Young relation. In addition, the results are both suit for the systems with time‐varying delay and the systems with constant delay. Finally, three numerical examples are given to illustrate the effectiveness of the proposed methods and two comparability examples are provided to prove the superiority of the algorithm.     

一类不确定系统的自适应滑模迭代学习控制

本文针对一类在有限时间内执行重复任务的不确定非线性系统状态跟踪问题,提出一种自适应滑模迭代学习控制方法,在存在初始偏移的情况下也能实现对参考轨迹的完全收敛.本文通过设计全饱和自适应迭代学习更新律,估计参数和非参数不确定性以及未知期望控制输入,并将估计值限制在指定界内,避免估计值的正向累加.文章设计的自适应滑模迭代学习控制方法对系统模型的信息需求少,在对系统非参数不确定性的上界估计时不需要Lipschitz界函数已知.本文给出严格的理论分析,证明闭环系统所有信号的一致有界性以及跟踪误差的一致收敛性,并通过仿真验证所提控制方法的有效性.     

An Exploration on Adaptive Iterative Learning Control for a Class of Commensurate High-order Uncertain Nonlinear Fractional Order Systems

This paper explores the adaptive iterative learning control method in the control of fractional order systems for the first time. An adaptive iterative learning control (AILC) scheme is presented for a class of commensurate high-order uncertain nonlinear fractional order systems in the presence of disturbance. To facilitate the controller design, a sliding mode surface of tracking errors is designed by using sufficient conditions of linear fractional order systems. To relax the assumption of the identical initial condition in iterative learning control (ILC), a new boundary layer function is proposed by employing Mittag-Leffler function. The uncertainty in the system is compensated for by utilizing radial basis function neural network. Fractional order differential type updating laws and difference type learning law are designed to estimate unknown constant parameters and time-varying parameter, respectively. The hyperbolic tangent function and a convergent series sequence are used to design robust control term for neural network approximation error and bounded disturbance, simultaneously guaranteeing the learning convergence along iteration. The system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapnov-like composite energy function (CEF) containing new integral type Lyapunov function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.      

Robust learning control for robotic manipulators with an extension to a class of non-linear systems

A robust learning control (RLC) scheme is developed for robotic manipulators by a synthesis of learning control and robust control methods. The non-linear learning control strategy is applied directly to the structured system uncertainties that can be separated and expressed as products of unknown but repeatable (over iterations) state-independent time functions and known state-dependent functions. The non-linear uncertain terms in robotic dynamics such as centrifugal, Coriolis and gravitational forces belong to this category. For unstructured uncertainties which may have non-repeatable factors but are limited by a set of known bounding functions as the only a priori knowledge, e.g the frictions of a robotic manipulator, robust control strategies such as variable structure control strategy can be applied to ensure global asymptotic stability. By virtue of the learning and robust properties, the new control system can easily fulfil control objectives that are difficult for either learning control or variable structure control alone to achieve satisfactorily. The proposed RLC scheme is further shown to be applicable to certain classes of non-linear uncertain systems which include robotic dynamics as asubset. Various important properties concerning learning control, such as the need for a resetting condition and derivative signals, whether using iterative control mode or repetitive control mode, are also made clear in relation to different control objectives and plant dynamics.     

Quantized Iterative Learning Control Design For Linear Systems Based On A 2‐D Roesser Model

This paper considers the problem of iterative learning control design for linear systems with data quantization. It is assumed that the control input update signals are quantized before they are transmitted to the iterative learning controller. A logarithmic quantizer is used to decode the signal with a number of quantization levels. Then, a 2‐D Roesser model is established to describe the entire dynamics of the iterative learning control (ILC) system. By using the sector bound method, a sufficient asymptotic stability condition for such a 2‐D system is established and then the ILC design is given simultaneously. The result is also extended to more general cases where the system matrices contain uncertain parameters. The effectiveness of the proposed method is illustrated by a numerical example.     

输入饱和非线性系统的周期自适应补偿学习控制

针对一类输入饱和不确定Brunovsky标准型非线性时滞系统,提出一种周期自适应跟踪补偿学习算法. 利用信号置换思想重组系统,基于最小公倍周期函数变换,将时滞时变项和不确定项合并为辅助参数,进而设计周期自适应学习律估计该辅助量,并利用饱和补偿器逼近和补偿超出饱和限的部分,由此构成综合控制器,以保证系统状态对有界期望值的跟踪,解决了饱和输入周期系统的重复迭代学习控制问题. 最后通过构造Lyapunov-Krasovskii复合能量函数的差分,计算证明了系统跟踪误差的收敛性和闭环信号值的有界性. 常见耦合非线性机械臂系统的力矩控制仿真,进一步验证了该算法的有效性.