基于动态神经网络的一类非线性不确定系统的自适应观测器

基于动态神经网络，对一类非线性不确定系统提出了相应的观测器设计方法，在观测设计中，充分考虑了不确定性和神经网络逼近误差对观测器性能的影响，增加了鲁棒控制项并设计了相应的参数自适应律，以保证良好的观测性能，神经网络的权值在线进行调整，而无需离线学习。     

一类基于神经网络非线性观测器的鲁棒故障检测和诊断

利用神经网络的非线性建模能力，对一类具有建模不确定项的非线性系统提出一种基于观测器的故障检测和诊断的方法。设计的观测器不仅能实现故障检测，而旦应用神经网络设计的故障估计器能在线估计系统中的故障向量。通过分析验证了该方法对系统中的建模误差和外部扰动具有良好的鲁棒性。仿真结果表明所提出的方法是有效的。     

基于观测器的水下机器人神经网络稳定自适应控制

给出了基于观测器的水下机器人神经网络自适应控制算法．控制算法由3部分组成：输出反馈控制、神经网络以及滑模项，其中输出反馈控制为了保证系统的初始稳定性；神经网络用于逼近系统的非线性动力学；滑模项用于补偿和抑制系统的外部扰动、神经网络逼近误差等．控制算法中所需要的速度量由状态观测器来提供．基于Lyapunov稳定理论给出了系统闭环稳定条件和稳定域．水池试验结果验证了算法的有效性．     

基于干扰观测器的一类不确定非线性系统鲁棒H∞控制

为了降低控制器对干扰的要求,基于干扰观测器提出一类多输入多输出不确定非线性系统的鲁棒H∞控制方法．将系统的内部不确定性和外部干扰组成复合干扰,设计基于小波神经网络的复合干扰观测器,并提出干扰观测器的参数调节方案使观测器能以高精度逼近复合干扰．同时在控制器中引入鲁棒控制项用来抑制观测器误差给系统带来的影响,所设计的控制器能使系统的跟踪误差小于一个给定的性能指标．最后给出一个仿真算例验证了本控制方案的有效性．     

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．     

离散线性系统高阶积分观测器设计

针对离散线性系统提出一类高阶积分观测器。并且显示这类观测器满足极点配置分离原理，同时给出了这类观测器的存在条件．基于Sylvester矩阵方程的显式参数化通解提出了这类观测器的参数化设计方法．该方法不仅给出了观测器增益矩阵的参数表达式，而且还提供了观测器系统矩阵左特征向量的参数表达式．该设计方法给出了所有的设计自由度，为实现系统的其他性能提供了方便且强有力的工具．数值例子说明了设计过程，并表明了该方法的有效性．     

基于观测器的机械手神经网络自适应控制

提出了一种基于观测器的机械手神经网络自适应轨迹跟随控制器设计方法,这里机 械手的动力学非线性假设是未知的,并且假设机械手仅有关节角位置测量.文中采用一个线 性观测器重构机械手的关节角速度,用神经网络逼近修正的机械手动力学非线性,改进系统 的跟随性能.基于观测器的神经网络自适应控制器能够保证机械手角跟随误差和观测误差的 一致终结有界性以及神经网络权值的有界性,最后给出了机械手神经网络自适应控制器-观 测器设计的主要理论结果,并通过数字仿真验证了所提方法的性能.     

基于模糊神经网络的非线性系统故障诊断

研究故障观测器优化设计,针对一类非线性动态系统,在考虑系统的输入输出包含外部扰动及建模误差等不确定性项[1]的情况下,为了提高所设计观测器对系统数学模型的在线跟踪能力从而进一步提高故障诊断的鲁棒性减少系统的误报警率,提出了基于模糊神经网络的诊断方法。利用神经网络以及模糊系统对非线性函数的无限逼近能力,设计了基于T-S模糊模型[2]的神经网络自适应观测器来拟合系统的非线性模型和系统的非线性故障特性。由Lyapunov稳定性方法获得调整观测器权重的规律。对所用改进方法的收敛性进行了证明,并通过仿真实例说明了诊断方法的有效性和使用性。     

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．     

用B-样条神经网络设计非线性观测器

对线性部分已知、非线性部分未知的一类非线性系统,提出了一种新的状态观测器 的设计方法.首先针对线性部分设计线性观测器,随后在线性观测器中加入非线性补偿项.该 补偿项先由"反卷积"的方式确定,再用B-样条神经网络拟合.对三个非线性系统设计了观测 器,通过与已有的解析方法进行比较,说明了该方法的优越性.     

Adaptive observers for unknown general nonlinear systems

Several neural network (NN) models have been applied successfully for modeling complex nonlinear dynamical systems. However, the stable adaptive state estimation of an unknown general nonlinear system from its input and output measurements is an unresolved problem. This paper addresses the nonlinear adaptive observer design for unknown general nonlinear systems. Only mild assumptions on the system are imposed: output equation is at least C(1) and existence and uniqueness of solution for the state equation. The proposed observer uses linearly parameterized neural networks (LPNNs) whose weights are adaptively adjusted, and Lyapunov theory is used in order to guarantee stability for state estimation and NN weight errors. No strictly positive real (SPR) assumption on the output error equation is required for the construction of the proposed observer.     

基于RBF神经网络的非线性系统故障诊断

针对一类模型未知的非线性动态系统,提出了一种基于神经网络在线估计结构的鲁棒故障诊断检测方法.系统中,仅输入输出可测,且包含输出不确定性项.该方法通过构造神经网络在线逼近结构来拟合该非线性系统模型和系统的非线性故障特性,建立系统的状态观测器.神经网络的权重调整规律由李亚普诺夫稳定性方法获得,系统的输出不确定性部分被用于获得诊断算法的阈值.最后,用Matlab/SIMULINK对的算法予以验证,结果表明本算法的合理性.     

基于改进神经网络的非线性系统观测器设计

根据非线性系统利用前馈网络的函数逼近能力,设计了一种神经网络观测器,并利用网络权值校正法,建立Lyapunov函数对观测器的稳定性进行了分析。为了加快训练速度,在训练网络时采用LM优化算法来实现,仿真结果不仅证明了所设计的神经网络观测器的有效性,还证实了神经网络改进算法后的优越性。     

Design of Asymptotic Estimators: An Approach Based on Neural Networks and Nonlinear Programming

A methodology to design state estimators for a class of nonlinear continuous-time dynamic systems that is based on neural networks and nonlinear programming is proposed. The estimator has the structure of a Luenberger observer with a linear gain and a parameterized (in general, nonlinear) function, whose argument is an innovation term representing the difference between the current measurement and its prediction. The problem of the estimator design consists in finding the values of the gain and of the parameters that guarantee the asymptotic stability of the estimation error. Toward this end, if a neural network is used to take on this function, the parameters (i.e., the neural weights) are chosen, together with the gain, by constraining the derivative of a quadratic Lyapunov function for the estimation error to be negative definite on a given compact set. It is proved that it is sufficient to impose the negative definiteness of such a derivative only on a suitably dense grid of sampling points. The gain is determined by solving a Lyapunov equation. The neural weights are searched for via nonlinear programming by minimizing a cost penalizing grid-point constraints that are not satisfied. Techniques based on low-discrepancy sequences are applied to deal with a small number of sampling points, and, hence, to reduce the computational burden required to optimize the parameters. Numerical results are reported and comparisons with those obtained by the extended Kalman filter are made     

Adaptive output feedback control methodology applicable to non-minimum phase nonlinear systems

An adaptive output feedback control methodology is developed for a class of uncertain multi-input multi-output nonlinear systems using linearly parameterized neural networks. The methodology can be applied to non-minimum phase systems if the non-minimum phase zeros are modeled to a sufficient accuracy. The control architecture is comprised of a linear controller and a neural network. The neural network operates over a tapped delay line of memory units, comprised of the system's input/output signals. The adaptive laws for the neural-network weights employ a linear observer of the nominal system's error dynamics. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. Simulations of an inverted pendulum on a cart illustrate the theoretical results.     

基于小波神经网络观测器的故障监测

首先针对一类Lipschitz非线性系统构造了实用稳定的故障观测器,然后研究如何有限神经元小波神经网络实现上述观测器,并通过数值结果验证了其在故障监测中的有效性。     

Fractional neural observer design for a class of nonlinear fractional chaotic systems

In this paper, a novel observer structure for nonlinear fractional-order systems is presented to estimate the states of fractional-order nonlinear chaotic system with unknown dynamical model. A new fractional error back-propagation learning algorithm is derived to adapt weights of the artificial neural network, by taking advantage of the Lyapunov stability strategy of fractional-order systems which is called Miattag–Leffler stability. The main contribution is the extension of neural observer for fractional dynamics in a way that satisfies Miattag–Leffler conditions. Observer design procedure guarantees the convergence of observer error to the neighborhood of zero. Furthermore, the robustness of the proposed estimator against uncertainties and external disturbances are the main benefits of the proposed method. Simulation results show the effectiveness and capabilities of the proposed observer.

     

一类非线性系统的神经网络自适应区间观测器设计

本文研究了一类单输入单输出非线性系统的神经网络自适应区间观测器设计问题. 针对由状态和输入所描述的未知非线性函数的界不可测, 现有的区间观测器方法并未有效地处理系统含有参数不确定性的未知非线性函数. 首先, 本文构造两个径向基函数神经网络来逼近未知非线性部分, 进而分别估计系统状态的上下界; 然后, 选择合适的Lyapunov函数, 采用网络权值校正和网络误差选择机制确保所设计的误差动态系统有界和非负性, 并证明了神经网络自适应区间观测器的稳定性; 最后, 通过仿真实例验证了所提出的神经网络自适应区间观测器的有效性.     

一类组合大系统的间接自适应控制

针对一类非线性组合大系统 ,提出一种用高阶神经网络逼近互联大系统的新型设计方法。首先用高阶神经网络逼近非线性组合大系统中的互联项 ,这样不仅可以解决大系统中最为复杂的互联项问题 ,且较以往采用的方法在工程上易于实现 ;然后基于高阶神经网络研究组合大系统的间接自适应控制问题。仿真结果表明了该方法的有效性。     

Composite adaptive locally weighted learning control for multi-constraint nonlinear systems

A composite adaptive locally weighted learning (LWL) control approach is proposed for a class of uncertain nonlinear systems with system constraints, including state constraints and asymmetric control saturation in this paper. The system constraints are tackled by considering the control input as an extended state variable and introducing barrier Lyapunov functions (BLFs) into the backstepping procedure. The system uncertainty is approximated by a composite adaptive LWL neural networks (NNs), in which a prediction error is constructed by using a series-parallel identification model, and NN weights are updated by both the tracking error and the prediction error. The update law with composite error feedback improves uncertainty approximation accuracy and trajectory tracking accuracy. The feasibility and effectiveness of the proposed approach have been demonstrated by formal proof and simulation results.