不确定非线性切换系统的鲁棒H∞控制

讨论了一类不确定非线性切换系统的鲁棒H∞控制问题．首先,基于多Lyapunov函数方法,设计状态反馈控制器以及切换律,使得对于所有允许的不确定性．相应的闭环系统渐近稳定又具有指定的L2-增益．该问题可解的充分条件以一组含有纯量函数的偏微分不等式形式给出,此偏微分不等式较一般Hamilton-Jacobi不等式更具可解性．所提出的方法不要求任何一个子系统渐近稳定．接着作为应用,借助混杂状态反馈策略讨论了非切换不确定非线性系统的鲁棒H∞控制问题．最后通过一个简单例子说明了控制设计方法的可行性．     

基于RBF神经网络的一类不确定非线性系统自适应H∞控制

基于RBF神经网络提出了一种H∞自适应控制方法.控制器由等效控制器和H∞控制器两部分组成.用RBF神经网络逼近非线性函数,并把逼近误差引入到网络权值的自适应律中用以改善系统的动态性能.H∞控制器用于减弱外部干扰及神经网络的逼近误差对跟踪的影响.所设计的控制器不仅保证了闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标.最后给出的算例验证了该方法的有效性.     

基于神经网络干扰观测器的一类不确定非线性MIMO系统H_∞跟踪控制

针对一类具有未知外部干扰及内部不确定性的非线性MIMO系统,提出了基于神经网络干扰观测器的鲁棒跟踪控制方法,用于降低控制器对干扰的要求.设计了基于神经网络的干扰观测器,以逼近由外部干扰、内部不确定性和子系统的交叉耦合组成的复合干扰.根据Lyapunov稳定性理论的参数更新律及所设计的控制器,保证了系统中所有信号的最终一致有界性,并获得了给定的跟踪性能指标.仿真结果证明了该方法的有效性.     

一类非线性系统的H∞鲁棒控制

考虑了一类带有扰动的仿射非线性系统的H∞控制问题，包括状态馈与动态输出反馈两种情形，我们基于HJI不等式的转换，直接给出了相应解的一种构造以及一种构造性判据，从而避免了通常的从数值求解HJI不等式的困难。     

基于自适应神经网络的一类不确定非线性系统的鲁棒H∞控制

针对一类不确定非线性多输入时变系统，提出了一种新的鲁棒H∞控制方案．通过引入2个自适应神经网络逼近器，提出了一个简化的Hamilton—Jaeobi—like不等式，并据此设计了非线性H∞控制器和匹配不确定项补偿控制器，消除了输入摄动项和估计器最优逼近误差的有界性假设．机器人系统的鲁棒跟踪控制仿真算例证实了所提出控制方案的有效性．     

非线性离散不确定系统的鲁棒H∞状态反馈控制

研究具有不确定非线性离散系统的鲁棒性能准则问题,基于二人零和动态对策理论,给出并证明了系统鲁棒稳定以及扰动衰减问题解存在的充分条件,通过求解离散时间Hamilton-jacobi-Isaacs方程给出了其鲁棒H∞状态反馈控制解。     

一类不确定非线性系统的鲁棒H∞控制

讨论了在自由系统和输入通道都带有有界不确定性的仿射非线性系统的鲁棒H∞控制问题,给出了状态反馈控制设计,对所有允许的不确定性它既能保证关于H∞模的干扰抑制品质,又能镇定该系统.同时指出如果一个相应的Hamilton-Jacobi不等式有非负解,则该不确定仿射非线性系统的鲁棒H∞控制问题可解.     

一类非线性不确定系统的鲁棒观测器设计

对于一类非线性不确定系统，给出一种基于观测器的鲁棒稳定控制器设计的新方法，它适用于一般匹配不确定系统，且对全维和降维两种观测器均进行了研究．设计实例表明，所设计的控制器反馈增益幅值较小、实现方便．     

一类不确定非线性系统的鲁棒H∞控制

在一种工程应用背景之下,考虑了一类非线性系统的鲁棒H∞控制问题,不确定性范数有界,基于H JI不等式研究了状态反馈控制器设计问题,使得闭环系统满足H∞干扰抑制性能要求.     

一类非线性不确定系统的鲁棒观测器设计

对于一类非线性不确定系统,给出一种基于观测器的鲁棒稳定控制器设计的新方法,它适用于一般匹配不确定系统,且对全维和降维两种观测器均进行了研究.设计实例表明,所设计的控制器反馈增益幅值较小、实现方便.     

Robust adaptive backstepping control for a class of uncertain nonlinear systems based on disturbance observers

In this paper, a novel robust adaptive control scheme for a class of uncertain nonlinear systems is proposed using disturbance observer and backstepping method.Firstly, a disturbance observer is developed using radial basis function(RBF) neural network.The parameter updated law of the RBF neural network is given for monitoring subsystem disturbance well.The robust adaptive control scheme is then presented with backstepping method based on the designed disturbance observer.Semiglobal uniform ultimate bounded...     

基于干扰观测器的一类奇异系统H∞控制

提出了一种基于干扰观测器的奇异系统鲁棒H_∞控制方法.外部干扰广泛存在于奇异系统中,为了降低其对系统的影响,设计了一种奇异系统干扰观测器以估计系统干扰.然后给出闭环系统相容的条件,设计一种基于干扰观测器的鲁棒控制器,并基于李雅普诺夫稳定性理论证明了闭环系统的渐近稳定性,通过设计指标函数得到闭环系统具有鲁棒性能的条件.相对于传统鲁棒控制方法,基于干扰观测器的方法降低了系统设计的保守性.最后,通过仿真实验验证了所提出方法的正确性和有效性.     

Robust H-infinity reliable control for a class of nonlinear uncertain neutral delay systems

This paper focuses on the robust H-infinity reliable control for a class of nonlinear neutral delay systems with uncertainties and actuator failures. We design a state feedback controller in terms of linear matrix inequality(LMI) such that the plant satisfies robust H-infinity performance for all adnfissible uncertainties, and actuator failures among a prespecified subset of actuators. An example is also given to illustrate the effectiveness of the proposed approach.     

一类多项式非线性系统鲁棒H∞控制

针对一类具有多项式向量场的仿射型不确定非线性系统,给出一种基于多项式平方和(sum of squares,SOS)技术的鲁棒H∞状态反馈控制器设计方法.该方法的优点在于控制器的设计避开了直接求解复杂的哈密尔顿-雅可比不等式(Hamilton Jacobi inequality,HJI)和构造Lyapunov函数带来的困难.将鲁棒稳定性分析和控制器设计问题转化为求解以Lyapunov函数为参数的矩阵不等式,该类不等式可利用SOS技术直接求解.此外,在前文基础上研究了基于SOS规划理论与S-procedure技术的局部稳定鲁棒H∞控制器设计方法.最后以非线性质量弹簧阻尼系统作为仿真算例验证该方法的有效性.     

Disturbance-observer-based sampled-data adaptive output feedback control for a class of uncertain nonlinear systems

In this paper, a sampled-data adaptive output feedback controller is proposed for a class of uncertain nonlinear systems with unmeasured states, unknown dynamics and unknown time-varying external disturbances. To approximate uncertain nonlinear functions, radial basis function neural networks (RBFNNs) are employed. The state observer and the disturbance observer (DO) are constructed to estimate the unmeasured state and the external disturbance, respectively. Then, the sampled-data adaptive output feedback controller and adaptive laws are designed by using the backstepping design technique. The allowable sampling period T is derived to guarantee that all states of the resulting closed-loop system are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to illustrate the effectiveness of the proposed approach.     

Adaptive fuzzy tracking control for a class of uncertain MIMO nonlinear systems using disturbance observer

In this paper,the adaptive fuzzy tracking control is proposed for a class of multi-input and multioutput(MIMO)nonlinear systems in the presence of system uncertainties,unknown non-symmetric input saturation and external disturbances.Fuzzy logic systems(FLS)are used to approximate the system uncertainty of MIMO nonlinear systems.Then,the compound disturbance containing the approximation error and the timevarying external disturbance that cannot be directly measured are estimated via a disturbance observer.By appropriately choosing the gain matrix,the disturbance observer can approximate the compound disturbance well and the estimate error converges to a compact set.This control strategy is further extended to develop adaptive fuzzy tracking control for MIMO nonlinear systems by coping with practical issues in engineering applications,in particular unknown non-symmetric input saturation and control singularity.Within this setting,the disturbance observer technique is combined with the FLS approximation technique to compensate for the efects of unknown input saturation and control singularity.Lyapunov approach based analysis shows that semi-global uniform boundedness of the closed-loop signals is guaranteed under the proposed tracking control techniques.Numerical simulation results are presented to illustrate the efectiveness of the proposed tracking control schemes.     

Sliding mode control with disturbance observer for a class of nonlinear systems

This paper is concerned with the stabilization problem for a class of nonlinear systems with disturbance. The disturbance model is unknown and the first derivative of disturbance is bounded. Firstly, a general disturbance observer is proposed to estimate disturbance approximatively. Secondly, since the bound of the disturbance observer error is unknown, an adaptive sliding mode controller is designed to guarantee that the state of system asymptotically converges to zero and the unknown bound can be adjusted by an adaptive law. Finally, an example is given to illustrate the effectiveness of the proposed method.     

Robust adaptive neural control for a class of uncertain MIMO nonlinear systems

In this paper, a novel robust adaptive neural control scheme is proposed for a class of uncertain multi-input multi-output nonlinear systems. The proposed scheme has the following main features: (1) a kind of Hurwitz condition is introduced to handle the state-dependent control gain matrix and some assumptions in existing schemes are relaxed; (2) by introducing a novel matrix normalisation technique, it is shown that all bound restrictions imposed on the control gain matrix in existing schemes can be removed; (3) the singularity problem is avoided without any extra effort, which makes the control law quite simple. Besides, with the aid of the minimal learning parameter technique, only one parameter needs to be updated online regardless of the system input–output dimension and the number of neural network nodes. Simulation results are presented to illustrate the effectiveness of the proposed scheme.     

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。