基于RBF神经网络观测器的非线性系统鲁棒故障检测方法

针对一类仿射非线性动态系统，提出一种基于网络非线性观测器的鲁棒故障检测与隔离的新方法，采用ＲＢＦ神经网络逼近观测器系统中的非线性项，提高了状态估计的精度，证明了状态估计误差稳定且渐近收敛到零；同时提出了一种新的网络权值调整指标方法，提高了神经网络故障分类器的泛化能力，从而保证该方法对监测系统的建模 外部扰动具有良好的鲁棒性。     

基于T-S 模糊模型的一类非线性网络控制系统故障检测

针对同时存在网络时延和数据包丢失的网络环境,研究了一类非线性网络控制系统的鲁棒故障检测问 题．基于不确定T-S 模糊模型描述的非线性网络控制系统模型,完成了网络环境下鲁棒故障检测观测器的设计,使 得残差信号对故障敏感而对外部扰动具有鲁棒性．构造Lyapunov-Krasovskii 函数,并引入一个积分不等式,给出了 使得观测器误差动态系统渐近稳定的充分条件．采用线性矩阵不等式技术将鲁棒故障检测问题转化为具有线性矩阵 不等式约束的凸优化问题求解．仿真算例验证了上述方法应用于此类系统的故障检测的有效性．     

基于观测器的非线性不确定离散系统的鲁棒模糊控制

研究了参数不确定及状态变量难以获取的非线性系统的鲁棒模糊控制问题.采用离散模糊T-S模型对离散非线性系统进行建模并建立了模糊观测器.用矩阵不等式的形式推导出了在Lyapunov意义下鲁棒镇定的充分条件.利用线性矩阵不等式(LMI)导出了模糊反馈增益和模糊观测器增益存在的充分条件.     

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。     

基于状态观测器的一类非线性系统的模糊鲁棒控制

利用T－S模型对一类非线性不确定系统进行模糊建模,在此基础上研究模糊鲁棒观测器及模糊状态鲁棒控制器的设计,并证明所设计的模糊鲁棒观测器和模糊状态鲁棒控制器具有全局渐近稳定性质。     

一类基于神经网络非线性观测器的鲁棒故障检测

针对一类仿射非线性动态系统,提出了一种基 于神经网络非线性观测器的鲁棒故障检测与隔离的新方法．该方法采用神经网络逼近观测器 系统中的非线性项,提高了状态估计的精度,并从理论上证明了状态估计误差稳定且渐近收 敛到零;另一方面引入神经网络分类器进行故障的模式识别,通过在神经网络输入端加入噪 声项来进行训练,提高神经网络的泛化逼近能力,从而保证对被监测系统的建模误差和外部 扰动具有良好的鲁棒性．最后,利用本文方法针对某型歼击机结构故障进行仿真验证,仿真 结果表明本文方法是有效的．     

基于扰动估计的飞行控制系统传感器故障重构

针对飞行控制系统的传感器故障重构问题,讨论了基于扰动估计的传感器故障检测与重构方法。首先,通过建立观测器,对已有系统的干扰进行估计;其次,通过干扰的估计值来建立系统的鲁棒观测器;最后,再通过对系统建立多个降维鲁棒观测器来隔离并重构传感器故障。采用某飞机横侧向模型进行仿真验证,仿真表明基于扰动估计的鲁棒观测器能够有效地对传感器故障进行隔离与重构。     

基于干扰观测器的一类不确定非线性系统自适应二阶动态terminal滑模控制

针对一类不确定非线性系统的跟踪控制问题,在考虑建模误差、参数不确定和外部干扰情况下,以其拥有良好的跟踪性能以及强鲁棒性为目标,提出基于回归扰动模糊神经网络干扰观测器(recurrent perturbation fuzzy neural networks disturbance observer,RPFNNDO)的鲁棒自适应二阶动态terminal滑模控制策略.将回归网络、模糊神经网络和sine-cosine扰动函数各自优势相结合,给出一种回归扰动模糊神经网络结构,提出RPFNNDO设计方法,保证干扰估计准确性;构造基于带有指数函数滑模面的二阶快速terminal滑模面,给出其控制器设计过程,避免了滑模到达阶段、传统滑模的抖振问题,采用具有指数收敛的鲁棒项抑制干扰估计误差对系统跟踪性能的影响,利用Lyapunov理论证明闭环系统的稳定性;将该方法应用于混沌陀螺系统同步控制仿真实验,结果表明所提方法的有效性.     

非线性关联大系统的鲁棒自适应观测器设计

针对一类存在未知参数、干扰和未建模动态的非线性关联大系统,提出了一种鲁棒自适应观测器.在观测器中对每个子系统引入一个动态信号来独立抑制未建模动态,同时用自适应非线性阻尼项来克服系统关联.用此观测器不需要估计未知参数及求解线性矩阵不等式.本文从理论上证明了所设计的观测器误差一致有界,并且通过恰当选择有关设计参数可使估计误差任意小.     

一类伴有部分解耦干扰的非线性系统故障诊断

针对Lipschitz非线性系统执行器故障检测和传感器故障估计问题,本文提出了一种基于H-/L_∞未知输入观测器的有限频域故障诊断策略.首先,将系统处理成包含传感器故障的增广系统.然后,将该系统的未知输入干扰分为可解耦与不可解耦两部分.针对可解耦部分,利用观测器匹配条件将其从估计误差中消除.针对不可解耦部分,设计L_∞指标抑制其对残差的影响并结合有限频域H-指标提高执行器故障检测灵敏度.接着,给出观测器存在的充分条件并将其转化为受LMIs约束的线性优化问题,实现了执行器故障的鲁棒检测及传感器故障的鲁棒估计.最后,结合仿真算例验证了所提方法的正确性与有效性.     

一种基于滑模—神经网络观测器的故障检测和诊断方法

本文针对一类非线性系统，提出了一种用于故障检测和诊断的滑模观测器方法．其 中，观测器中的滑模项保证了该系统在无故障情况时的鲁棒性，并且系统运行的滑动区域提供了故障检测的条件．当检测出故障之后，观测器中的故障估计部分被启动，利用RBF神经网络估计故障，从而能在线辨识故障的形态．仿真结果验证了该方法的有效性．     

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.     

Variable high-gain disturbance observer design with online adaption of observer gains embedded in numerical integration

In this paper, a variable gain design approach for the high-gain disturbance observer, called Proportional-Integral-Observer (PI-Observer), is proposed to solve the problem of choosing suitable observer gains. The high-gain PI-Observer is successfully applied to estimate unknown inputs of systems together with the system states. It is known that reasonable estimations of unknown inputs can only be derived using high observer gains. On the other hand, extremely large gains will cause serious problems with respect to measurements noise and unmodeled dynamics. According to the analysis of the estimation quality regarding to the factors which influence the estimation results, the optimal level of observer gains is changing during the estimation, an online adaption for the observer gains is therefore developed. The designed PI-Observer, called Advanced PI-Observer (API-Observer), will use changing observer gains from the adaption algorithm, which is proved to give stable estimation error dynamics. Simulation results from an elastic beam example are shown to illustrate the implementation of the API-Observer.     

Robust adaptive observer for nonlinear systems with unmodeled dynamics

Unmodeled dynamics exist in almost all applications of observers due to the impossibility of using exact and detailed models. It is highly desired that the observers can dominate the effects of unmodeled dynamics independently to prevent the state estimations from diverging and to get the precise estimations. Based on adaptive nonlinear damping, this paper presents a robust adaptive observer for multiple-input multiple-output nonlinear systems with unknown parameters, uncertain nonlinearities, disturbances and unmodeled dynamics. The observer only has one adaptive parameter no matter how high the order of the system is and how many unknown parameters there are. With the proposed observer, neither estimating the unknown parameters or solving linear matrix inequalities is needed. It is shown that the state estimation error is uniformly bounded and can be made arbitrarily small.     

Nonlinear adaptive control using networks of piecewise linearapproximators

Presents a stable nonparametric adaptive control approach using a piecewise local linear approximator. The continuous piecewise linear approximator is developed and its universal approximation capability is proved. The controller architecture is based on adaptive feedback linearization plus sliding mode control. A time varying activation region is introduced for efficient self-organization of the approximator during operation. We modify the adaptive control approach for piecewise linear approximation and self-organizing structures. In addition, we provide analyses of asymptotic stability of the tracking error and parameter convergence for the proposed adaptive control scheme with the online self-organizing structure. The method with a deadzone is also discussed to prevent a high-frequency input which might excite the unmodeled dynamics in practical applications. The application of the piecewise linear adaptive control method is demonstrated by a computational simulation.     

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

This research deals with developing an intelligent trajectory tracking control approach for an aircraft in the presence of internal and external disturbances. Internal disturbances including actuators faults, unmodeled dynamics, and model uncertainties as well as the external disturbances such as wind turbulence significantly affect the performance of the common trajectory tracking control approaches. There are several fault‐tolerant control approaches in the literature to overcome the effects of specific actuator or sensor faults during the flight. However, trajectory tracking control of an air vehicle in the presence of unexpected faults and simultaneous presence of wind turbulence is still a challenging problem. In this paper, an intelligent neural network‐based model predictive control structure is proposed, where the prediction model is updated in each iteration based on a novel proposed online sequential multimodel structure. A hybrid offline‐online learning algorithm is adopted in the introduced online sequential multimodel structure to identify the time‐varying dynamics of the system. The proposed control structure can satisfactorily deal with unexpected actuator faults and structural damages as well as unmodeled dynamics and wind turbulence. The stability of the closed‐loop system is proved under some realistic assumptions. The simulation results demonstrate the high capability of the proposed approach for trajectory tracking control of a conventional aircraft in the simultaneous presence of system faults and external disturbances.     

Adaptive Monitoring and Accommodation of Nonlinear Actuator Faults in Positive Real Infinite Dimensional Systems

We consider a class of positive real infinite dimensional systems which are subjected to incipient actuator faults. The actuator fault is modeled as a time varying transition from an initial (linear or even nonlinear) map into another unknown nonlinear map at the onset of the fault occurrence. An infinite dimensional adaptive detection observer is utilized to generate a residual signal in order to detect the fault occurrence and to assist in the fault accommodation. This is done via an automated control reconfiguration which utilizes information on the new actuator map and adjusts the controller via a right inverse of the new actuator map. A robust modification is utilized in order to avoid false alarms caused by unmodeled dynamics. An example is included to illustrate the applicability of the proposed detection scheme.     

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.     

基于故障跟踪估计器的非线性时滞系统故障诊断

提出一种可有效检测和估计一类非线性时滞系统故障的故障跟踪估计器.根据预测控制和迭代学习控制的思想,在所选取的优化时域长度内,通过迭代算法调节故障跟踪估计器中的可调参数,使之逼近系统中实际发生的故障.与以往基于观测器的故障诊断方法不同的是,故障跟踪估计器可同时检测和估计系统中发生的故障,而且针对不同类型的故障亦有很好的适应性.仿真结果表明了所提出算法的可行性和有效性.