非线性离散时间系统的自适应模糊补偿控制

针对一类非线性离散时间系统,提出一种自适应模糊逻辑补偿控制方案.控制律由跟踪控制律和逼近误差补偿控制律两部分组成,利用模糊逻辑系统对系统参数扰动和外界干扰进行自适应补偿,由模糊滑模控制律实现对模糊逻辑系统逼近误差的进一步补偿.所设计的控制器可保证闭环系统一致最终有界.将该控制器用于月球探测车动态转向系统中,仿真结果表明了该方法的有效性.     

时变时滞随机非线性系统的自适应神经网络跟踪控制

针对一类具有时变时滞的不确定随机非线性严格反馈系统的自适应跟踪问题,利用Razumikhin引理和backstepping方法,提出一种新的自适应神经网络跟踪控制器.该控制器可保证闭环系统的所有误差变量皆四阶矩半全局一致最终有界,并且跟踪误差可以稳定在原点附近的邻域内.仿真例子表明所提出控制方案的有效性.     

基于神经网络MIMO非线性系统自适应输出反馈控制

针对一类具有对象不确定和外部干扰的MIMO(多输入多输出)非线性系统提出了自适应鲁棒输出跟踪控制方案.使用了高斯径向基神经网络自适应补偿对象非线性,高增益观测器被用来估计不能直接测量的输出导数.此方法所设计的控制器不仅保证闭环系统稳定,而且所有状态有界以及跟踪误差一致终值有界.仿真结果充分表明了该方案的有效性和可行性.     

永磁同步电机伺服系统的自适应模糊滑模控制

针对永磁同步电机伺服系统的跟踪控制问题,提出了一种基于扰动观测器的自适应模糊滑模控制方法.通过扰动观测器估计等效扰动,改善了系统的动态性能和稳态性能,并且只需要等效扰动的变化有界,而不是为零,放宽了要求;根据模糊控制原理引入3条模糊规则,在保证滑模条件的前提下有效地削弱了抖振;采用自适应策略估计模糊系统参数的最优值,简化了控制器的设计.实验结果表明,与常规自适应模糊滑模控制相比,本文提出的控制方法不仅能够有效地减小跟踪误差,而且能够改善参数估计过程,保证了参数估计的有界性.     

不确定离散系统的神经网络自适应控制方法研究

本文研究了不确定离散系统的神经网络自适应控制器的设计。因为它不需要假设系统状态是可测的,一个观测器用来估计不可测状态。与现有离散系统的结果相比,该控制器具有较少的直接自适应参数。因此,可以很方便地实现工程算法。利用Lyapunov分析方法,所有的闭环系统的信号是保证最终有界（UUB）,并且能够实现系统输出跟踪参考信号到有界紧集。一个仿真例子,验证了该方法的有效性。     

状态观测的未知死区非线性系统的自适应神经网络跟踪控制

研究一类包含不确定项和未知死区特性的严格反馈系统跟踪控制问题.首先,设计状态观测器估计不可测量的系统状态;然后,利用RBF神经网络逼近未知的系统动态;最后,基于Backstepping技术构造自适应神经网络输出反馈控制器,并减少更新参数以减轻运算负荷.所提出的控制器可以保证闭环系统中所有信号半全局最终一致有界,跟踪误差能收敛到零值小的领域内.两个仿真例子进一步验证了所提出方法的有效性.     

基于自适应模糊状态观测器的非线性系统鲁棒故障检测

针对仿射非线性系统,采用自适应模糊系统进行非线性补偿,提出了一种基于自适应模糊状态观测器的鲁棒故障检测方法.为确保观测器对嗓声干扰和系统参数不确定误差的鲁棒性,给出了闭环系统在有界嗓声干扰和系统参数不确定误差下的稳定性定理,并进行了详尽的证明.仿真结果表明该方法的有效性及对嗓声干扰和系统参数不确定误差的鲁棒性.     

不确定非线性时滞关联大系统自适应分散容错控制

针对一类不确定非线性时滞关联大系统,提出了一种基于时滞代换的自适应分散容错控制方案.该方案采用模糊逻辑系统作为逼近器,提出了时滞代换的方法处理系统未知时滞关联函数,并结合自适应技术处理代换误差和逼近误差.与现有方法相比,本文方法能在线补偿所有四种类型的执行器故障,系统控制器的设计也不再依赖于时滞假设条件,同时还可保证闭环系统所有信号全局一致最终有界.仿真结果进一步验证了本文方法的有效性.     

控制方向未知的SISO非仿射系统间接自适应模糊输出反馈控制

针对一类单输入单输出(SISO)非仿射非线性系统控制方向未知时出现的控制器奇异问题,提出了一种间接自适应模糊控制方案.利用中值定理将非仿射系统转化为仿射系统,通过模糊逻辑系统逼近该仿射系统中的未知函数,并构造模糊控制器,同时利用Lyapunov稳定性定理设计自适应律,最终克服了控制器的奇异问题;在此基础上,通过构造观测器估计跟踪误差,设计输出反馈自适应模糊控制器,解决了状态不可测时系统控制器设计难题,采用Lyapunov稳定性定理证明控制器能使得跟踪误差收敛同时闭环系统所有信号均有界.仿真结果验证了所设计控制方案的可行性与有效性.     

一类基于模糊逻辑系统的非线性不确定系统自适应观测器设计

利用模糊逻辑系统具有充分利用专家语言信息和逼近连续函数的性质, 分析和研究了一类具有未知不确定界的非线性系统的自适应状态观测器设计问题. 观测器和自适应律的构成直接利用了系统的数学结构信息和模糊逻辑系统对不确定性的输出信息, 在较弱的假设条件下, 这种观测器与被控系统状态间的误差及各参数估计误差一致终极有界. 最后的仿真实例说明了所采用的方法的有效性.     

State observer-based adaptive fuzzy output-feedback control for a class of uncertain nonlinear systems

This paper aims to develop state observer-based adaptive fuzzy control techniques for controlling a class of uncertain nonlinear systems with bounded external disturbances. An adaptive fuzzy observer is proposed to estimate the system state variables. It is shown that the observation errors obtained from the observer are uniformly ultimately bounded. Applying the estimated system state for design of an output-feedback controller, the uniformly ultimate boundedness of the tracking errors for the resulting closed-loop system can be guaranteed. A typical robot arm system is employed in our simulation studies, and the results demonstrate the usefulness and effectiveness of the proposed techniques for controlling nonlinear systems with bounded external disturbances.     

HGO-based decentralised indirect adaptive fuzzy control for a class of large-scale nonlinear systems

In this article, a novel high gain observer (HGO)-based decentralised indirect adaptive fuzzy controller is developed for a class of uncertain affine large-scale nonlinear systems. By the combination of fuzzy logic systems and an HGO, the state variables are not required to be measurable. The proposed feedback and adaptation mechanisms guarantee that each subsystem is able to adaptively compensate for interconnections and disturbances with unknown bounds. It is ascertained using a singular perturbation method that all the signals of the closed-loop large-scale system stand uniformly ultimately bounded and the tracking errors converge to tunable neighbourhoods of the origin. Simulation results of correlated double inverted pendulums substantiate the effectiveness of the proposed controller.     

基于自适应模糊滑模控制的船舶航向控制器设计

针对船舶运动系统中固有的非线性、模型不确定性和风、浪、流等的干扰.提出了自适应模糊滑模控制(AFSMC)策略解决船舶的航向控制问题.通过采用模糊逻辑系统逼近系统未知函数,将滑模控制技术与自适应模糊控制技术相结合,设计了船舶航向AFSMC控制器.在滑模边界层内应用PI (proportional-integral)控制代替滑模控制中的切换项,削弱了滑模控制带来的抖振现象.借助李亚普诺夫函数证明了船舶运动系统中的信号都一致有界并利用Barbalat引理证明了跟踪误差渐近收敛到零.在参数摄动和外界干扰情况下进行了航向保持与改变仿真试验,采用AFSMC控制器得到了与无摄动和无干扰情况下相似的输出响应.实验结果表明,所提控制器能有效地处理系统不确定性和外界干扰,控制性能良好,具有很强的鲁棒性.     

Observer-based variable universe adaptive fuzzy controller without additional dynamic order

A high-precision fuzzy controller, based on a state observer, is developed for a class of nonlinear single-input-single-output (SISO) systems with system uncertainties and external disturbances. The state observer is introduced to resolve the problem of the unavailability of state variables. Assisted by the observer, a variable universe fuzzy system is designed to approximate the ideal control law. Being auxiliary components, a robust control term and a state feedback control term are designed to suppress the influence of the lumped uncertainties and remove the observation error, respectively. Different from the existing results, no additional dynamic order is required for the control design. All the adaptive laws and the control law are built based on the Lyapunov synthesis approach, and the signals involved in the closed-loop system are guaranteed to be uniformly ultimately bounded. Simulation results performed on Duffing forced oscillation demonstrate the advantages of the proposed control scheme.     

Observer-based adaptive fuzzy backstepping control of uncertain nonlinear pure-feedback systems

In this paper,a new fuzzy adaptive control approach is developed for a class of SISO uncertain pure-feedback nonlinear systems with immeasurable states.Fuzzy logic systems are utilized to approximate the unknown nonlinear functions;and the filtered signals are introduced to circumvent algebraic loop systems encountered in the implementation of the controller,and a fuzzy state adaptive observer is designed to estimate the immeasurable states.By combining the adaptive backstepping technique,an adaptive fuzzy output feedback control scheme is developed.It is proven that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded(SGUUB),and the observer and tracking errors converge to a small neighborhood of the origin by appropriate choice of the design parameters.Simulation studies are included to illustrate the efectiveness of the proposed approach.     

Decentralized adaptive robust control for a class of large scale systems with uncertainties in the interconnections

The problem of decentralized control is considered for a class of time-varying large scale systems with uncertainties and external disturbances in the interconnections. In this paper, the upper bounds of the uncertainties and external disturbances are assumed to be unknown. The adaptation laws are proposed to estimate such unknown bounds, and by making use of the updated values of these unknown bounds, a class of decentralized linear and non-linear state feedback controllers are constructed. It is shown that by employing the proposed decentralized non-linear state feedback controllers, the solutions of the resulting adaptive closed-loop large scale system can be guaranteed to be uniformly bounded, and the states are uniformly asymptotically stable. By using the decentralized linear state feedback controllers, one can guarantee the uniform ultimate boundedness of the resulting adaptive closed-loop large scale system. Finally, a numerical example is given to demonstrate the validity of the results.     

Observer-based hybrid adaptive fuzzy control for affine and nonaffine uncertain nonlinear systems

This paper presents a novel observer-based hybrid adaptive fuzzy controller for affine and nonaffine nonlinear systems with external disturbance. The suggested design is so easy and does not need a mathematical model for system under control and also it is very simple, efficient and robust. Based on the adaptive method and the system states observer, an observer-based adaptive fuzzy method is proposed to control an uncertain nonlinear system. Also, a supervisory controller term is employed to attenuate the residual error to a desired level and compensate the both uncertainties and observer errors. Although proposed control method needs the uncertainties to be bounded, it does not need this bound to be identified. Stability of the proposed method is shown based on Lyapunov theory and also the strictly positive real condition if all the implicated signals are uniformly bounded. Finally, in our simulation studies, to demonstrate the usefulness and efficiency of the suggested technique, an uncertain nonlinear system is employed.

     

Adaptive fuzzy decentralized control for nonlinear large-scale systems based on high-gain observer

An adaptive fuzzy decentralized backstepping output-feedback control approach is proposed for a class of nonlinear large-scale systems with completely unknown functions,the interconnections mismatched in control inputs,and without the measurements of the states.Fuzzy logic systems are employed to approximate the unknown nonlinear functions,and an adaptive high-gain observer is developed to estimate the unmeasured states.Using the designed high-gain observer,and combining the fuzzy adaptive control theory with backstepping approach,an adaptive fuzzy decentralized backstepping output-feedback control scheme is developed.It is proved that the proposed control approach can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded(SUUB),and that the observer errors and the tracking errors converge to a small neighborhood of the origin by appropriate choice of the design parameters.Finally,a simulation example is provided to show the eectiveness of the proposed approach.     

Adaptive and robust controller design for uncertain nonlinear systems via fuzzy modeling approach

The issue for designing robust adaptive stabilizing controllers for nonlinear systems in Takagi-Sugeno fuzzy model with both parameter uncertainties and external disturbances is studied in this paper. It is assumed that the parameter uncertainties are norm-bounded and may be of some structure properties and that the external disturbances satisfy matching conditions and, besides, are also norm-bounded, but the bounds of the external disturbances are not necessarily known. Two adaptive controllers are developed based on linear matrix inequality technique and it is shown that the controllers can guarantee the state variables of the closed loop system to converge, globally, uniformly and exponentially, to a ball in the state space with any pre-specified convergence rate. Furthermore, the radius of the ball can also be designed to be as small as desired by tuning the controller parameters. The effectiveness of our approach is verified by its application in the control of a continuous stirred tank reactor.     

Adaptive fuzzy decentralized output feedback control for stochastic nonlinear large‐scale systems using DSC technique

In this paper, an adaptive fuzzy decentralized backstepping output feedback control approach is proposed for a class of uncertain large‐scale stochastic nonlinear systems without the measurements of the states. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed for estimating the unmeasured states. Using the designed fuzzy state observer, and by combining the adaptive backstepping technique with dynamic surface control technique, an adaptive fuzzy decentralized output feedback control approach is developed. It is shown that the proposed control approach can guarantee that all the signals of the resulting closed‐loop system are semi‐globally uniformly ultimately bounded in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing appropriate design parameters. A simulation example is provided to show the effectiveness of the proposed approaches. Copyright © 2011 John Wiley & Sons, Ltd.