模糊时滞系统的输出反馈控制器设计

利用模糊T-S模型对一类非线性时滞系统进行建模;在此基础上,设计出了模糊静态输出反馈控制器和模糊动态输出反馈控制器,并利用Lyapunov-Razumikhin引理和线性矩阵不等式证明了系统渐近稳定的充分条件,通过求解一系列线性矩阵不等式,得到了反馈增益矩阵。     

基于观测器的模糊时滞系统指数稳定的一种设计方法

研究了一类模糊时滞系统的指数稳定问题.首先利用T-S模型对非线性不确定性时滞系统进行建模,在此基础上设计了基于观测器的模糊状态反馈控制器,通过巧妙选取Lyapunov函数给出了模糊闭环时滞系统的条件及稳定裕度且模糊反馈增益和模糊观测器增益可通过求解线性矩阵不等式获得.     

一阶双曲型偏微分方程的模糊边界控制

对于一类半线性的双曲型偏微分方程的模糊边界控制问题,通过模糊控制方法,将半线性的偏微分方程系统精确表示为T-S模糊偏微分方程模型.因为控制器仅仅分布于边界上,所以基于T-S模糊偏微分方程模型而设计的模糊边界控制器将更容易执行,并且能够保证闭环系统指数稳定.然后利用Lyapunov方法将给出的闭环系统指数稳定的充分条件转化为求解线性不等式的问题.最后,通过仿真实例说明了模糊边界控制的有效性.     

基于T-S模型的Lurie混沌系统的模糊控制

针对Lurie混沌控制系统,进行了T-S模糊建模和模糊控制器设计,从而实现了Lurie混沌系统的稳定.在用T-S模糊模型精确重构Lurie系统结构的基础上,利用反馈同步思想,基于并行分布补偿(PDC)技术,得到了简单且易实现的控制器.仿真结果验证了该控制方法的有效性.     

基于LMI方法的一类非线性模糊脉冲系统的鲁棒模糊控制

通过推广一般T-S模糊模型定义了一类非线性模糊脉冲系统.基于线性矩阵不等式(LMI)方法提出了一种鲁棒模糊控制新方案.采用并行分布补偿(PDC)的基本思想设计状态反馈控制器,并利用Lyapunov方法理论证明闭环系统全局指数稳定.最后基于LMI方法,将鲁棒模糊控制器的设计问题转化为线性矩阵不等式问题(LMIP).仿真表明本方法的有效性.     

多机电力系统直接自适应输出反馈模糊控制

针对多机电力系统励磁控制模型,考虑电力系统的状态不完全可测及多变量、非线性等特点,以T-S模糊逻辑系统直接逼近控制器,设计出基于状态观测器的直接自适应输出反馈模糊控制器,并通过李亚普诺夫函数进行了稳定性证明.算法具有很好的鲁棒性和动态性能,仿真结果表明所设计的控制器能够快速有效地改善系统在大干扰下的暂态稳定性.     

基于模糊双线性模型的非线性系统的跟踪控制

研究一种基于T-S模糊双线性系统的跟踪控制器设计及稳定性分析.使用分布并行补偿法(PDC)设计了模糊控制器,得到模糊双线性系统跟踪控制渐近稳定的充分条件,仿真结果验证了该方法改进了闭环系统的性能.     

一类基于模糊T-S模型的有执行器故障的自适应容错跟踪控制

研究了一类带有执行器故障的T-S模糊系统的容错跟踪控制问题。设计中,把模糊控制与自适应控制相结合,提出了一种新的容错控制方法。该控制器由正常控制器和一个自适应控制器组成,能够使得闭环系统稳定,故障状态模型渐近跟踪正常模型,并获得优化的控制性能。应用Lyapunov函数和线性矩阵不等式方法,给出和证明了带有执行器故障的T-S模糊系统的稳定的充分条件。仿真结果进一步验证了所提出的方法的有效性。     

具有时延和乱序网络化系统的动态矩阵控制北大核心CSCD

研究了具有时延和数据包乱序的网络化系统的动态矩阵控制问题.首先,通过时序分析,给出了控制器端和执行器端处理乱序的方法,建立了包含时延和乱序的网络化系统模型.然后,针对所建立的模型,提出了改进的动态矩阵控制算法,给出了控制器的设计方法.进一步,通过对系统的稳定性分析,导出了保证闭环系统稳定的充分条件.最后,通过仿真验证了所提方法的有效性.     

一类模糊T-S模型互联系统的自适应容错跟踪控制

针对一类带有执行器故障的T-S模糊互联的容错跟踪控制问题,提出了一种模糊自适应容错控制器。该控制器由一个模糊控制器和一个自适应控制器组成,模糊控制器能够保证系统没有故障时闭环系统渐近稳定,而自适应控制器能够补偿系统的执行器故障。所提出的容错控制方法不但使得闭环系统渐近稳定、系统的输出渐近跟踪给定的参考信号,并获得H∞控制性能。最后应用Lyapunov函数和线性矩阵不等式的方法,给出和证明了带有执行器故障的T-S模糊互联系统的稳定的充分条件。仿真结果进一步验证了所提出方法的有效性。     

Adaptive fuzzy sliding-mode control for multi-input multi-output chaotic systems

This paper describes an adaptive fuzzy sliding-mode control algorithm for controlling unknown or uncertain, multi-input multi-output (MIMO), possibly chaotic, dynamical systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal sliding-mode controller, and the robust controller compensates the difference between the fuzzy controller and the ideal one. The parameters of the fuzzy system, as well as the uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the asymptotic stability and tracking of the controlled system. The effectiveness of the proposed method is shown by applying it to some well-known chaotic systems.     

Self-organizing adaptive wavelet CMAC backstepping control system design for nonlinear chaotic systems

A novel self-organizing wavelet cerebellar model articulation controller (CMAC) is proposed. This self-organizing wavelet CMAC (SOWC) can be viewed as a generalization of a self-organizing neural network and of a conventional CMAC, and it has better generalizing, faster learning and faster recall than a self-organizing neural network and a conventional CMAC. The proposed SOWC has the advantages of structure learning and parameter learning simultaneously. The structure learning possesses the ability of on-line generation and elimination of layers to achieve optimal wavelet CMAC structure, and the parameter learning can adjust the interconnection weights of wavelet CMAC to achieve favorable approximation performance. Then a SOWC backstepping (SOWCB) control system is proposed for the nonlinear chaotic systems. This SOWCB control system is composed of a SOWC and a fuzzy compensator. The SOWC is used to mimic an ideal backstepping controller and the fuzzy compensator is designed to dispel the residual of approximation errors between the ideal backstepping controller and the SOWC. Moreover, the parameters of the SAWCB control system are on-line tuned by the derived adaptive laws in the Lyapunov sense, so that the stability of the feedback control system can be guaranteed. Finally, two application examples, a Duffing–Holmes chaotic system and a gyro chaotic system, are used to demonstrate the effectiveness of the proposed control method. The simulation results show that the proposed SAWCB control system can achieve favorable control performance and has better tracking performance than a fuzzy neural network control system and a conventional adaptive CMAC.     

Fuzzy delayed output feedback synchronization for time-delayed chaotic systems

In this paper, we propose a new fuzzy delayed output feedback synchronization (FDOFS) method for time-delayed chaotic systems. Based on Lyapunov–Krasovskii theory, T–S fuzzy model, and delayed feedback control scheme, the FDOFS controller is designed and an analytic expression of the controller is shown. The proposed controller can guarantee asymptotical synchronization of both drive and response systems. The FDOFS controller can be obtained by solving the linear matrix inequality (LMI) problem. A numerical example for time-delayed Lorenz system is presented to demonstrate the validity of the proposed FDOFS method.     

A novel of fuzzy PSS based on new objective function in multimachine power system

This article proposed a new control strategy based on Takagi–Sugeno fuzzy model for deceasing the power system oscillation. This controller is based on the parallel distributed compensation structure, the stability of the whole closed‐loop model is provided using a general Lyapunov‐Krasovski functional. Also, in this article, a new objective function has been considered to test the proposed Fuzzy Power System Stabilizer in different load conditions which increase the system damping after the system undergoes a disturbance. So, for testing the effectiveness of the proposed controller, the damping factor, damping ratio, and a combination of the damping factor and damping ratio were analyzed and compared with the proposed objective function. The effectiveness of the proposed strategy has been used over 16 machine 68 bus power system. The eigenvalue analysis and nonlinear time domain simulation results proof the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Complexity 21: 288–298, 2016     

Adaptive type-2 fuzzy backstepping control of uncertain fractional-order nonlinear systems with unknown dead-zone

A new problem of adaptive type-2 fuzzy fractional control with pseudo-state observer for commensurate fractional order dynamic systems with dead-zone input nonlinearity is considered in presence of unmatched disturbances and model uncertainties; the control scheme is constructed by using the backstepping and adaptive technique. To avoid the complexity of backstepping design process, the dynamic surface control is used. Also, Interval type-2 Fuzzy logic systems (IT2FLS) are used to approximate the unknown nonlinear functions. By using the fractional adaptive backstepping, fractional control laws are constructed; this method is applied to a class of uncertain fractional-order nonlinear systems. In order to better control performance in reducing tracking error, the PSO algorithm is utilized for tuning the controller parameters. Stability of the system is proven by the Mittag–Leffler method. It is shown that the proposed controller guarantees the boundedness property for the system and also the tracking error can converge to a small neighborhood of the origin. The efficiency of the proposed method is illustrated with simulation examples.     

Fuzzy关系方程干解在模糊在线控制系统中的应用研究

讨论了怎样快速求出Fuzzy关系方程干解的方法.探讨了该方法在模糊在线控制系统中的应用,同时给出了如何快速构建经济适用的动态模糊控制器的设计方法.     

Adaptive synchronization of T–S fuzzy chaotic systems with unknown parameters

This paper presents a fuzzy model-based adaptive approach for synchronization of chaotic systems which consist of the drive and response systems. Takagi–Sugeno (T–S) fuzzy model is employed to represent the chaotic drive and response systems. Since the parameters of the drive system are assumed unknown, we design the response system that estimates the parameters of the drive system by adaptive strategy. The adaptive law is derived to estimate the unknown parameters and its stability is guaranteed by Lyapunov stability theory. In addition, the controller in the response system contains two parts: one part that can stabilize the synchronization error dynamics and the other part that estimates the unknown parameters. Numerical examples, including Duffing oscillator and Lorenz attractor, are given to demonstrate the validity of the proposed adaptive synchronization approach.     

Fuzzy Entropy： Axiomatic Definition and Neural Networks Model

The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.     

Observer-based robust adaptive variable universe fuzzy control for chaotic system

A novel observer-base output feedback variable universe adaptive fuzzy controller is investigated in this paper. The contraction and expansion factor of variable universe fuzzy controller is on-line tuned and the accuracy of the system is improved. With the state-observer, a novel type of adaptive output feedback control is realized. A supervisory controller is used to force the states to be within the constraint sets. In order to attenuate the effect of both external disturbance and variable parameters on the tracking error and guarantee the states to be within the constraint sets, a robust controller is appended to the variable universe fuzzy controller. Thus, the robustness of system is improved. By Lyapunov method, the observer-controller system is shown to be stable. The overall adaptive control algorithm can guarantee the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. In the paper, we apply the proposed control algorithms to control the Duffing chaotic system and Chua’s chaotic circuit. Simulation results confirm that the control algorithm is feasible for practical application.