一类不确定非仿射非线性系统自适应模糊控制

针对一类非仿射非线性系统提出了自适应模糊控制方法,该方法把不确定非线性系统表示为定常线性子系统加非线性项的形式,然后采用模糊逻辑系统设计补偿器来消除非线性项的影响。引入时变死区函数对模糊逻辑系统中的未知参数进行自适应调节,并对时变死区设计了自适应律。证明了该方法可使闭环系统的所有信号均有界,且使跟踪误差收敛到原点的小邻域内。仿真结果表明了该方法的有效性。     

一类非线性系统的间接自适应输出反馈模糊控制

针对一类未知非线性系统,提出一种输出反馈控制方法。首先在假设系统状态已知的情况下设计状态反馈控制器,实现跟踪性能。然后在系统状态不完全可测的情况下,通过设计高增益观测器对系统的状态进行估计,实现输出反馈控制器设计。最后证明所设计的输出反馈控制器可获得状态反馈控制器所取得的最大最小问题的跟踪性能。     

一类严格反馈非线性系统的间接自适应模糊控制

针对一类不确定严格反馈非线性系统,设计了间接自适应模糊控制方法．该方法用模糊逻辑系统逼近设计过程中的未知函数,基于时变宽度死区对模糊逻辑系统中的未知参数进行自适应调节,并对时变死区宽度设计了自适应律．证明了该方法能使闭环系统的所有信号有界,且可使跟踪误差收敛到原点的小邻域内．仿真算例验证了该方法的有效性．

     

严格反馈型非仿射非线性系统的自适应模糊控制

针对一类具有严格反馈形式的非仿射非线性受扰系统,提出了基于backstepping方法的自适应模糊控制.该算法仅要求模糊逻辑系统逼近误差范数有界,引入监督控制补偿系统逼近误差和外界干扰,保证闭环系统所有信号一致有界,跟踪误差一致渐近稳定.将R(o)ssle混沌系统作为仿真对象,仿真结果表明了该方法的有效性.     

一类非三性系统的间接自适应输出反馈模糊控制

针对一为未知非线性系统,提出一种输出反馈控制方法。首先在假设系统状态已知的情况下设计状态反馈控制器,实现跟踪性能。然后在系统状态下不完全可测的情况下,通过设计高增益观测器对系统的状态进行估计,实现输出反馈控制器设计。最后证明所设计的输出反馈控制器可获得状态反馈控制器所取得的最大最小问题的跟踪性能。     

严格反馈非线性系统的自适应模糊控制

针对一类不确定非线性系统,基于backstepping方法提出了一种新的鲁棒自适应模糊控制器设计方案。该方案通过引入最优逼近误差的自适应补偿项和新的鲁棒项,削减建模误差和参数估计误差的影响,从而在稳定性分析中取消了要求逼近误差平方可积或逼近误差的上确界已知的条件。理论分析证明了闭环系统状态有界,跟踪误差收敛到零的较小邻域内。仿真结果表明了该方法的有效性。     

一般严格反馈型非线性系统的自适应控制

研究一般严格反馈型非线性系统的控制问题．假设系统的对象模型、状态均未知,只有输出是可测的．应用自适应模糊神经推断系统辨识对象模型,状态观测器设计为Luenberger型,控制器由反步控制、变结构控制和3层神经网络直接控制综合而成．理论分析和仿真研究都说明此方案能够有效地控制只有输出可测的一般严格反馈型非线性系统．     

一类大系统的间接自适应分散模糊控制

针对一类未知非线性大系统,将模糊控制、模糊逻辑系统及滑模控制相结合,提出了一种间接自适应模糊控制策略,仿真结果证明了所提出的算法是有效的.     

一类非线性系统的自适应滑模模糊控制

针对一类具有多个子系统的欠驱动非线性系统提出了一种自适应滑模模糊控制方法.首先通过分析模糊控制与边界层滑模控制的相似性,提出了滑模模糊控制方法;然后根据滑模面斜率和各子系统控制对于系统动态性能的影响,分别采用模糊推理根据系统状态自动地实时调节滑模面斜率和各子系统在系统控制中的作用;最后通过简单的滑模模糊控制器实现对具有多个子系统的欠驱动非线性系统的控制.将该方法应用于吊车的运输控制中,仿真结果证明了其有效性和鲁棒性.     

一类严格反馈非线性系统的快速鲁棒控制

针对一类具有外部扰动的高阶严格反馈非线性系统,提出一种新型反正切扩张状态观测器并证明其观测误差有界。结合Lyapunov稳定性理论,设计一种鲁棒控制器,并证明了系统跟踪误差有界。文章给出一个仿真例子检验了所提方法的有效性。     

Direct adaptive fuzzy control of nonlinear strict-feedback systems

This paper focuses on adaptive fuzzy tracking control for a class of uncertain single-input /single-output nonlinear strict-feedback systems. Fuzzy logic systems are directly used to approximate unknown and desired control signals and a novel direct adaptive fuzzy tracking controller is constructed via backstepping. The proposed adaptive fuzzy controller guarantees that the output of the closed-loop system converges to a small neighborhood of the reference signal and all the signals in the closed-loop system remain bounded. A main advantage of the proposed controller is that it contains only one adaptive parameter that needs to be updated online. Finally, an example is used to show the effectiveness of the proposed approach.     

Stable adaptive fuzzy control for MIMO nonlinear systems

In this paper, an indirect adaptive fuzzy control scheme is presented for a class of multi-input and multi-output (MIMO) nonlinear systems whose dynamics are poorly understood. Within this scheme, fuzzy systems are employed to approximate the plant’s unknown dynamics. In order to overcome the controller singularity problem, the estimated gain matrix is decomposed into the product of one diagonal matrix and two orthogonal matrices, a robustifying control term is used to compensate for the lumped errors, and all parameter adaptive laws and robustifying control term are derived based on Lyapunov stability analysis. The proposed scheme guarantees that all the signals in the resulting closed-loop system are uniformly ultimately bounded (UUB). Moreover, the tracking errors can be made small enough if the designed parameter is chosen to be sufficiently large. A simulation example is used to demonstrate the effectiveness of the proposed control scheme.     

一类死区非线性系统的自适应模糊控制设计

为了实现对具有时变摄动死区非线性系统的跟踪控制,本文提出了一种基于自适应模糊逼近器的Backstepping控制方法。该方法通过将死区特性合理分解,并将自适应模糊逼近器嵌入到Backstepping设计步骤中,逐步递推得到控制律。所提出的控制方法适用于高阶非线性系统,并且不要求被控系统满足匹配条件;所采用的模糊逼近器是非线性参数化的,亦即不要求其模糊基函数是完全确定已知的,从而降低了对先验知识的依赖性。为了得到未知参数的自适应律,本文先应用Taylor级数展开式将具有非线性关系的未知参数相互分离,使其呈现线性关系,然后根据Lyapunov稳定性定理给出在线可调参数的自适应律。此外,所设计的自适应律是对与未知参数向量的范数相关的变量进行在线调节,这样可以有效减少需要在线调节的参数数量,从而降低了控制器的在线计算负担,提高了系统的响应速度和控制精度。本文给出的控制设计能够有效地克服死区特性对系统性能的影响,使得闭环系统所有信号均指数收敛到原点的指定邻域内,系统输出可以按给定的精度跟踪参考信号。最后,本文用一个仿真实例验证了所给控制方法的有效性。     

基于观测器的非线性互连系统的自适应模糊控制

针对一类不确定非线性MIMO互连系统,提出一种自适应模糊控制算法.通过设计观测器来估计系统的状态,因此不要求假设系统的状态是可测的.给出的自适应律只对不确定界进行在线调节,从而大大减轻了在线计算负担.该算法能够保证闭环系统的所有信号是一致有界的,并且跟踪误差指数收敛到一个小的零邻域内.仿真结果表明了算法的可行性.     

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.     

Observer-based direct adaptive fuzzy control of uncertain nonlinear systems and its applications

A direct adaptive fuzzy control algorithm is developed for a class of uncertain SISO nonlinear systems. In this algorithm, it doesn’t require to assume that the system states are measurable. Therefore, it is needed to design an observer to estimate the system states. Compared with the numerous alternative approaches with respect to the observer design, the main advantage of the developed algorithm is that on-line computation burden is alleviated. It is proven that the developed algorithm can guarantee that all the signals in the closed-loop system are uniformly ultimately bounded and the tracking error converges to a small neighborhood around zero. The simulation examples validate the feasibility of the developed algorithm. Recommended by Editorial Board member Zhong Li under the direction of Editor Young-Hoon Joo. This work is supported by National Natural Science Foundation of China under grant 60674056, 60874056, and the Foundation of Educational Department of Liaoning Province (2008312). Yan-Jun Liu received the B.S. degree in Applied Mathematics from Shenyang University of Technology in 2001. He received the M.S. degree in Control Theory and Control Engineering from Shenyang University of Technology in 2004 and the Ph.D. degree in Control Theory and Control Engineering from Dalian University of Technology, China, in 2007. His research interests include fuzzy control theory, nonlinear control and adaptive control. Shao-Cheng Tong received the B.S. degree in Department of Mathematics from Jinzhou Normal College, China, in 1982. He received the M.S. degree in Department of Mathematics from Dalian Marine University in 1988 and the Ph.D. degree in Control Theory and Control Engineering from Northeastern University, China, in 1997. His research interests include fuzzy control theory, nonlinear control, adaptive control, and system identification etc. Wei Wang received the B.S. degree in Department of Automation from Northeastern University, China, in 1982. He received the M. S. degree in Department of Automation from Northeastern University in 1984 and the Ph.D. degree in Department of Automation from Northeastern University, China, in 1988. His research interests include adaptive predictive control, intelligent control, and production scheduling method etc. Yong-Ming Li received the B.S. degree in Applied Mathematics from Liaoning University of Technology in 2004. He received the M.S. degree in Applied Mathematics from Liaoning University of Technology in 2007. His research interests include fuzzy control theory, nonlinear control and adaptive control.     

Observer-based adaptive fuzzy control for a class of nonlinear time-delay systems

An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.     

Direct adaptive fuzzy control of a class of MIMO non-affine nonlinear systems

An adaptive fuzzy control approach is proposed for a class of multiple-input–multiple-output (MIMO) nonlinear systems with completely unknown non-affine functions. The global implicit function theorem is first used to prove the existence of an unknown ideal implicit controller that can achieve the control objectives. Within this scheme, fuzzy systems are employed the approximate the unknown ideal implicit controller, and robustifying control terms are used to compensate the approximation errors and external disturbances. The adjustable parameters of the used fuzzy systems are deduced from the stability analysis of the closed-loop system in the sense of Lyapunov. To show the efficiency of the proposed controllers, two simulation examples are presented.     

多变量非线性系统的间接模糊输出反馈自适应控制

针对一类多输入多输出非线性不确定系统,提出一种基于观测器的模糊间接自适应控制方法,并基于李亚普诺夫函数方法,导出了输出反馈控制律以及参数的自适应律,证明了整个控制方案不但能保证闭环系统稳定,而且取得了良好的跟踪控制性能。     

Approximation-based adaptive fuzzy control for a class of non-strict-feedback stochastic nonlinear systems

This paper considers the problem of adaptive fuzzy control of a class of single-input/single-output （SISO） nonlinear stochastic systems in non-strict-feedback form. Fuzzy logic systems are used to approximate the uncertain nonlinearities and backstepping technique is utilized to construct an adaptive fuzzy controller. The proposed controller guarantees that all the signals in the resulting closed-loop system are bounded in probability. The main contribution of this note lies in providing a control strategy for a class of nonlinear systems in non- strict-feedback form. Simulation result is used to test the effectiveness of the suggested approach.