非线性系统的鲁棒自适应模糊控制

针对反馈线性化后的不确定非线性对象,提出了一种鲁棒自适应控制方法.采用模糊模型逼近不确定项,用逐步后推技术构造出自适应律,在线调节模糊模型的参数,同时处理存在估计误差时的干扰抑制问题.该方法还用于感应电动机速度控制的仿真.用李亚普诺夫方法证明了整个系统的稳定性和鲁棒性.该方法适用于任意有界时变干扰,通过模糊模型还可以加入对干扰的经验知识.     

ADAPTIVE ROBUST CONTROL OF UNCERTAIN SYSTEMS WITH TIME‐VARYING STATE DELAY

In this paper, a new adaptive robust control scheme is developed for a class of uncertain dynamical systems with time‐varying state delay, unknown parameters and disturbances. By incorporating adaptive techniques into the robust control method, we propose a continuous adaptive robust controller which guarantees the uniform boundedness of the system and at the same time, the regulating error enters an arbitrarily designated zone in a finite time. The proposed controller is independent of the time‐delay, hence it is applicable to a class of dynamical systems with uncertain time delays. The paper includes simulation studies demonstrating the performance of the proposed control scheme.     

具有不确定性的非线性系统的变结构控制

本文研究了具有不确定性非线性系统的鲁棒输出跟踪控制问题，考虑了对参数、建模不确定性及干扰的鲁棒性。这里的不确定性不需满足通常的匹配条件。通过构造一个Ｌｙａｐｕｎｏｖ函数，我们得到一个变结构控制器，并且证明了输出跟踪误差的收敛性。最后，给出了计算机仿真，结果表明本文提出的方法是有效的。     

基于神经网络的非线性系统稳定自适应控制

本文针对一类具有未知非线性函数和未知虚拟系数非线性函数的二阶非线性系统 ,提出了一种基于神经网络的稳定自适应输出跟踪控制方法 .用李雅普诺夫稳定性分析方法证明了本文的神经网络自适应控制器能够使受控系统稳定 ,并使输出跟踪误差随时间趋于无穷而收敛到零 .仿真算例证明了该算法的有效性     

一类非线性系统的稳定神经网络自适应控制

利用神经网络作为非线性系统的模型，研究了一类非线性系统的神经网络自适应控制问题，设计出的自适应控制器具有如下的特点：（１）网络仅值是基于参考误差信号学习的投影算法来调节，这样可保证权值的有界性；（２）为了减小神经网络参数估计误差对跟踪误差的影响，提出了根据参考误差信号实时修正神经网络输入的方法。仿真结果对该控制方案进行了验证。     

ADAPTIVE SLIDING MODE BACKSTEPPING CONTROL OF NONLINEAR SYSTEMS WITH UNMATCHED UNCERTAINTY

This paper considers an adaptive backstepping algorithm for designing the control for a class of nonlinear continuous uncertain processes with disturbances that can be converted to a parametric semi‐strict feedback form. Sliding mode control using a combined adaptive backstepping sliding mode control (SMC) algorithm, is also studied. The algorithm follows a systematic procedure for the design of adaptive control laws for the output tracking of nonlinear systems with matched and unmatched uncertainty.     

严格反馈随机非线性系统风险灵敏度输出反馈控制器设计

研究了一类严格反馈随机非线性系统的输出反馈设计问题.在无限时区风险灵敏度指标下,应用积分反推(integrator backstepping)技术,设计了控制器.所设计的控制器能够保障对任意风险灵敏度系数具有任意小的指标,并且闭环系统为概率意义下有界的.特别地,所设计的控制器还能保证控制器的平衡条件.仿真例子验证了理论结果的正确性.     

基于自组织模糊CMAC网络的非线性系统鲁棒自适应跟踪控制

基于自组织模糊CMAC(SOFCMAC)神经网络,提出了一种非线性模型参考神经网络增广逆系统鲁棒自适应跟踪控制方法.该方法的特点是通过S0FCMAC神经网络在线修正由于建模误差、不确定因素等引起的非线性系统逆误差,使得系统输出准确跟踪参考模型输出.SOFCMAC的权值调整规律由Lyapunov稳定性理论导出.文中证明了非线性闭环系统的稳定性.仿真例子表明了本文方法的有效性.     

线性离散时变系统的鲁棒自适应控制

利用滑模控制的概念,提出一种线性离散时变系统的自适应控制设计方法.在等价控制信号外,用两个补偿项代替切换信号,避免了传统滑模控制中的抖动现象.文中证明了自适应闭环系统的稳定性.     

A ROBUST ADAPTIVE CONTROL DESIGN FOR A CLASS OF UNCERTAIN NONLINEAR MARKOVIAN JUMP SYSTEMS

A direct robust adaptive nonlinear control scheme for a class of uncertain nonlinear Markovian jump systems with nonlinear state‐dependent uncertainty is presented. The proposed scheme is Lyapunov‐based and guarantees the global uniform ultimate boundedness of the closed‐loop system in the mean sense. A numerical example is given to demonstrate the validity of the results.     

一类非最小相位非线性系统的神经网络自适应控制策略

针对一类非最小相位非线性系统，基于线性化和前馈补偿的思想，提出了神经自适应控制算法。得到了前馈控制律的闭环稳定性分析，并给出仿真结果。     

GLOBAL ROBUST SERVOMECHANISM OF A CLASS OF NONLINEAR SYSTEMS USING OUTPUT FEEDBACK

The robust servomechanism problem (alternatively, output regulation problem) of the class of nonlinear systems in lower triangular form has been extensively studied in recent years. The semi‐global solution was first given by either state feedback or output feedback. The global solution by state feedback was given very recently. However, the global solution by output feedback has long been an open problem. In this paper, we present a set of solvability conditions of the global robust servomechanism problem for this class of nonlinear systems by output feedback.     

INDIRECT ADAPTIVE CONTROL FOR NONLINEAR SYSTEMS WITH SYSTEM PERTURBATION

This paper presents an indirect adaptive control scheme for a class of input-output linearizable nonlinear systems subjected to system perturbations. System parameters are unknown and estimated recursively by a parameter estimator to obtain approximate system output and output derivatives, and then to derive an adaptive control law. In the parameter estimator, a dead-zone approach is used to avoid the parameter drift problem. A positive switching gain is also set to decrease the dead-zone value to obtain better output tracking performance. Under some assumptions, the indirect adaptive control scheme is proved to be stable.     

DIRECT ADAPTIVE CONTROL FOR NONLINEAR MATRIX SECOND‐ORDER SYSTEMS WITH TIME‐VARYING AND SIGN‐INDEFINITE DAMPING AND STIFFNESS OPERATORS

A direct adaptive control framework for a class of nonlinear matrix second‐order systems with time‐varying and sign‐indefinite damping and stiffness operators is developed. The proposed framework guarantees global asymptotic stability of the closed‐loop system states associated with the plant dynamics without requiring any knowledge of the system nonlinearities other than the assumption that they are continuous and bounded. The proposed adaptive control approach is used to design adaptive controllers for suppressing thermoacoustic oscillations in combustion chambers.     

DIRECT ADAPTIVE FEEDBACK DESIGN FOR LINEAR DISCRETE‐TIME UNCERTAIN SYSTEMS

In this paper, we develope a direct adaptive control framework for linear discrete‐time uncertain MIMO systems, based on assumptions that the system is controllable and the difference of the unknown system matrix from the stable solution is bounded by a given value. The proposed framework is Lyapunov‐based, and the controller guarantees adaptive stabilization. In addition, the adaptive laws are characterized by means of Kronecker calculus. Furthermore, the results can easily be extended to time‐varying cases, where the deviation from the stable solution is bounded, and the controllability assumption holds. The controller not only tolerates plant deviation, but also guarantees asymptotical stability of the closed‐loop system. Two numerical examples are provided to demonstrate the performance of the controller.     

空间机械臂的鲁棒复合自适应控制

对于本体姿态受控而位置不受控的空间机械臂系统,考虑存在参数不确定性及非参数不确定性,首先建立了系统的估计模型,并且提出了一种鲁棒复合自适应控制方法,其参数适应律由估计误差和跟踪误差共同决定.证明了这种方法不仅可维持系统的全局渐近稳定,而且还可快速收敛和减小跟踪误差.     

一类模糊非线性系统自适应输出反馈控制

针对一类未知非线性系统,利用模糊逻辑系统、H∞控制和高增益观测器,提出了一种模糊自适应输出反馈控制方法.证明了所设计的输出反馈控制器可以获得状态反馈控制器的性能.仿真结果证明了所提出方法是有效的.     

非线性系统的鲁棒采样控制

研究在汽车转向控制中遇到的、具有输出约束的一类非线性不确定系统的鲁棒采样控制和鲁棒采样最优控制问题,结果表示为一些矩阵不等式.最后基于线性矩阵不等式,给出了一个迭代算法和算例.数值计算实例证明了该算法的有效性.     

采样非线性系统的动态神经网络稳定自适应控制

研究了一类采样数据非线性系统的动态神经网络稳定自适应控制方法.不同于静态神经网络自适应控制,动态神经网络自适应控制中神经网络用于逼近整个采样数据非线性系统,而不是动态系统中的非线性分量.系统的控制律由神经网络系统的动态逆、自适应补偿项和神经变结构鲁棒控制项组成.神经变结构控制用于保证系统的全局稳定性,并加速动态神经网络系统的适近速度.证明了动态神经网络自适应控制系统的稳定性,并得到了动态神经网络系统的学习算法.仿真研究表明,基于动态神经网络的非线性系统稳定自适应控制方法较基于静态神经网络的自适应方法具有更好的性能.     

LMI APPROACH TO ROBUST FILTERING FOR DISCRETE TIME‐DELAY SYSTEMS WITH NONLINEAR DISTURBANCES

This paper investigates the problem of robust filtering for a class of uncertain nonlinear discrete‐time systems with multiple state delays. It is assumed that the parameter uncertainties appearing in all the system matrices reside in a polytope, and that the nonlinearities entering into both the state and measurement equations satisfy global Lipschitz conditions. Attention is focused on the design of robust full‐order and reduced‐order filters guaranteeing a prescribed noise attenuation level in an H∞ or l₂‐l∞ sense with respect to all energy‐bounded noise disturbances for all admissible uncertainties and time delays. Both delay‐dependent and independent approaches are developed by using linear matrix inequality (LMI) techniques, which are applicable to systems either with or without a priori information on the size of delays.