Variable Structure Control with chattering elimination and guaranteed stability for a generalized T-S model

In this paper, a fuzzy based Variable Structure Control (VSC) with guaranteed stability is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. The main contribution of this work is that, firstly, new functions for chattering reduction and error convergence without sacrificing invariant properties are proposed, which is considered the main drawback of the VSC control. Secondly, the global stability of the controlled system is guaranteed.The well known weighting parameters approach, is used in this paper to optimize local and global approximation and modeling capability of T-S fuzzy model.A one link robot is chosen as a nonlinear unstable system to evaluate the robustness, effectiveness and remarkable performance of optimization approach and the high accuracy obtained in approximating nonlinear systems in comparison with the original T-S model. Simulation results indicate the potential and generality of the algorithm. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved with the proposed FLC-VSC controller. The effectiveness of the proposed controller is proven infront of disturbances and noise effects.     

A new approach to fuzzy estimation of Takagi-Sugeno model and its applications to optimal control for nonlinear systems

An efficient approach is presented to improve the local and global approximation and modelling capability of Takagi-Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy. The main problem is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the use of the T-S method because this type of membership function has been widely used during the last two decades in the stability, controller design and are popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S method with optimized performance in approximating nonlinear functions. A simple approach with few computational effort, based on the well known parameters’ weighting method is suggested for tuning T-S parameters to improve the choice of the performance index and minimize it. A global fuzzy controller (FC) based Linear Quadratic Regulator (LQR) is proposed in order to show the effectiveness of the estimation method developed here in control applications. Illustrative examples of an inverted pendulum and Van der Pol system are chosen to evaluate the robustness and remarkable performance of the proposed method and the high accuracy obtained in approximating nonlinear and unstable systems locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity and generality of the algorithm.     

基于T-S模型的非线性系统模糊聚类辨识方法

提出一种基于T-S模型的非线性系统模糊聚类辨识方法,对T-S模糊模型的前提部分和结论部分进行分开辨识,既简化该模型的辨识步骤,又提高它的泛化能力,同时也解决了T-S模糊模型随辨识系统复杂程度提高而规则数增大的问题。对一个非线性系统辨识的仿真结果验证了这种模糊聚类辨识方法的有效性。     

MULTIVARIABLE FUZZY GENERALIZED PREDICTIVE CONTROL

A Takagi-Sugeno (T-S) fuzzy model is used to express non-linear dynamic systems with time-delay in this paper, and an on-line identification algorithm is presented regarding its parameters and structures. A multivariable fuzzy generalized predictive control approach is proposed based on the identified fuzzy model by means of the generalized predictive control principle. The closed-loop stability is analyzed in detail. A simulation study for the multivariable load system of a boiler-turbine unit shows that the approach is superior to convention load control systems.     

基于T-S模型的Chua混沌系统模糊变结构控制

针对Chua混沌系统这一复杂的非线性系统给出一种基于T-S模型的模糊变结构控制律设计。首先采用T-S模糊动态模型描述非线性系统,得到混沌系统的全局模糊模型;然后采用Lyapunov稳定性理论设计出确保模糊动态模型全局渐近稳定的变结构控制器,将模糊控制与成熟的线性变结构控制相结合,来解决非线性系统控制问题。仿真验证了方案的有效性。模糊控制器简单,规则少。     

Robust H-infinity fuzzy control for uncertainMarkovian jump nonlinear singular systems withwiener process

This paper deals with the problems of robust stochastic stabilization and H-infinity control for Markovian jump nonlinear singular systems with Wiener process via a fuzzy-control approach. The Takagi-Sugeno (T-S) fuzzy model is employed to represent a nonlinear singular system. The purpose of the robust stochastic stabilization problem is to design a state feedback fuzzy controller such that the closed-loop fuzzy system is robustly stochastically stable for all admissible uncertainties. In the robust H-infinity control problem, in addition to the stochastic stability requirement, a prescribed performance is required to be achieved. Linear matrix inequality (LMI) sufficient conditions are developed to solve these problems, respectively. The expressions of desired state feedback fuzzy controllers are given. Finally, a numerical simulation is given to illustrate the effectiveness of the proposed method.     

基于LMI的不确定离散模糊时滞系统鲁棒控制

利用T-S模糊模型，讨论了一类具有状态时滞和控制输入时滞的不确定离散非线性系统鲁棒控制问题,基于Lyapunov稳定性理论，导出了系统采用线性矩阵不等式表示的时滞相关型鲁棒镇定充分条件，并设计了相应的状态反馈模糊控制器．仿真结果证明了所提出方法的有效性和可行性.     

T-S模糊系统的二次稳定

应用LMI(线性矩阵不等式)方法,研究了T-S模糊系统二次稳定性及控制器设计问题.首先,通过考虑模糊系统隶属函数的性质,将原系统进行变换,给出了T-S模糊系统二次稳定的新条件,并提出了基于LMI的控制器设计方法.与现有结果相比,该方法不仅考虑了各子系统间的关系,还考虑到隶属函数的性质,计算量和保守性较小.仿真算例验证了其有效性.     

一种模糊系统稳定性的分析方法

针对T-S型模糊系统进行局部稳定性分析．首先指出模糊系统全局渐近稳定充分条件的不足，证明了此条件不是必要条件；然后将模糊集合的隶属度函数代入模糊系统表达式，得到一个特殊的非线性离散系统，从系统分析的区域、判定稳定的条件及稳定范围3方面确定稳定区域；最后对不稳定的系统给出了不稳定判据．仿真实例证明了这种方法的有效性．该方法对系统稳定点附近局部稳定性的判定有着很强的实用价值．     

前提不匹配的模糊时滞系统的稳定与控制

研究了基于Takagi-Sugeno（T-S）模糊模型描述的非线性时滞系统的稳定性与控制问题。选择一个最近提出的基于自由权矩阵的积分不等式,以线性矩阵不等式（LMIs）形式给出了保守性较小的时滞依赖的稳定性准则。基于前提不匹配策略,结合Finsler引理,提出了更为灵活的模糊状态反馈控制器的设计方法,该方法不要求控制器和系统分享共同的前提隶属函数和规则数目。最后,给出两个仿真算例,证明了所提理论的先进性和有效性。     

Robust PI controller design for nonlinear systems via fuzzymodeling approach

The design problem of proportional and proportional-plus-integral (PI) controllers for nonlinear systems is studied. First, the Takagi-Sugeno (T-S) fuzzy model with parameter uncertainties is used to approximate the nonlinear systems. Then a numerically tractable algorithm based on the technique of iterative linear matrix inequalities is developed to design a proportional (static output feedback) controller for the robust stabilization of the system in T-S fuzzy model. Next, we transform the problem of PI controller design to that of proportional controller design for an augmented system and thus bring the solution of the former problem into the configuration of the developed algorithm. Finally, the proposed method is applied to the design of robust stabilizing controllers for the excitation control of power systems. Simulation results show that the transient stability can be improved by using a fuzzy PI controller when large faults appear in the system, compared to the conventional PI controller designed by using linearization method around the steady state     

基于T-S模糊模型的离散混沌系统变结构控制

研究了离散混沌系统模糊变结构控制问题.采用T-S模糊模型描述离散混沌系统,将离散混沌系统模糊化为局部线性模型.依据Lyapunov稳定性定理和线性系统变结构控制趋近律设计方法,设计了一种新型的离散变结构控制器,该控制器不仅能保证局部线性模型渐近稳定,而且能确保模糊动态模型全局渐近稳定.利用Matlab对确定Henon系统和不确定Henon系统进行数值仿真,结果表明所设计的控制器不但有效,而且具备很强的鲁棒性.     

Robust fuzzy control for uncertain discrete-time nonlinear Markovian jump systems without mode observations

This paper studies the robust fuzzy control problem of uncertain discrete-time nonlinear Markovian jump systems without mode observations. The Takagi and Sugeno (T-S) fuzzy model is employed to represent a discrete-time nonlinear system with norm-bounded parameter uncertainties and Markovian jump parameters. As a result, an uncertain Markovian jump fuzzy system (MJFS) is obtained. A stochastic fuzzy Lyapunov function (FLF) is employed to analyze the robust stability of the uncertain MJFS, which not only is dependent on the operation modes of the system, but also directly includes the membership functions. Then, based on this stochastic FLF and a non-parallel distributed compensation (non-PDC) scheme, a mode-independent state-feedback control design is developed to guarantee that the closed-loop MJFS is stochastically stable for all admissible parameter uncertainties. The proposed sufficient conditions for the robust stability and mode-independent robust stabilization are formulated as a set of coupled linear matrix inequalities (LMIs), which can be solved efficiently by using existing LMI optimization techniques. Finally, it is also demonstrated, via a simulation example, that the proposed design method is effective.     

基于模糊动态模型的多变量系统模糊控制

采用模糊动态模型对多变量复杂非线性系统进行模糊控制.首先针对局部线性动态模 型设计状态反馈控制器,然后利用模糊推理确定整个系统的控制;在一系列局部模型通过模糊 隶属函数连接得到的连续的全局模型中,全面考虑其它关联子系统对标称线性系统的摄动,并 利用大系统分散控制关联稳定性的概念和方法,得到了闭环模糊系统稳定的充分条件.仿真例 子验证了该设计方法的正确性.     

鲁棒极点配置约束下的模糊控制系统设计

针对一类利用T-S模糊模型近似描述的不确定非线性系统,给出了一种具有鲁棒极点配置功能的模糊控制器和模糊状态观测器的设计方法.首先,利用并行分配补偿(PDC)设计思想和基于线性矩阵不等式(LMI)的鲁棒极点配置理论,得到了使整个闭环系统全局渐近稳定并满足希望的动态性能的充分条件.然后将这些条件转化为标准的LMI问题.最后将该设计方法应用于倒立摆的平衡控制中,验证了本方法的有效性.     

一类模糊时滞系统指定衰减度的鲁棒控制

研究了一类非线性时滞系统基于模糊T-S模型的鲁棒镇定问题,所考虑的不确定时滞系统含有时变未知但有界的状态时滞,首先利用Razumikhin定理和Lyapunov定理,得出了由模糊T-S模型描述的非线性时滞系统鲁棒稳定且具有指定衰减度的判据,其次得到了具有指定衰减度的无记忆状态反馈控制律存在的充分条件及相应的控制器设计方法,该条件被进一步等价地转化为一个线性矩阵不等式的可解性问题,所设计的控制器确保了闭环系统具有指定衰减度鲁棒稳定。     

拓扑切换下IT2 T-S模糊非线性多智能体系统全局逆最优控制

针对连续非线性多智能体系统的全局最优协同控制问题,本文提出了模糊输出反馈和逆最优方法的分布式一致性最优控制律和相应的控制策略.首先,通过一种区间2型T-S (interval type 2 Takagi-Sugeno IT2 T-S)模糊模型将非线性系统等价转化为线性系统.其次,基于逆最优方法设计了全局最优协同控制律和相应的模糊输出反馈控制策略,智能体间仅仅通过局部通信,即可实现拓扑切换下非线性多智能体系统的二次性能全局最优控制,且系统的收敛速度大大提高.基于局部稳定性理论给出了全局逆最优控制的充要条件.最后,通过MATLAB算例验证所提方法的正确性和可行性.     

不确定隶属函数T-S模糊控制器设计与稳定分析

针对许多非线性系统存在的结构不确定和难以用精确数学模型表达等问题,在研究基本Takagi-Sugeno(T-S)模糊模型的基础上,通过增加一个隶属函数自由变量,利用线性矩阵不等式(LMI)设计了一个使得闭环系统渐进稳定的状态反馈控制器,并给出稳定条件.所得到的条件充分利用了前件变量隶属函数的结构信息和后件局部子系统之间的相互关系,降低了常规T-S模糊系统的稳定性条件的保守性和求解难度.通过仿真实例验证了该方法的可行性和有效性.     

Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering. First, an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule. Then, compared to traditional prediction-based ones, two types of fuzzy set-membership filters are proposed to effectively improve filtering performance, where the structure of both filters consists of two parts: prediction and filtering. Under the locally Lipschitz continuous condition of membership functions, unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error. Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state. Finally, the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.      

Imperfect premise matching controller design for T-S fuzzy systems under network environments

This paper focuses on an imperfect premise matching controller design for T-S fuzzy systems under network environments. Different with the traditional parallel distribution compensation (PDC) method, the same premises between the PDC controller and the T-S fuzzy systems are no longer needed again in the proposed method. Under consideration of the unmatched grades of membership in the networked T-S fuzzy systems, a unified T-S fuzzy model is firstly proposed, in which a networked state-feedback fuzzy controller with communication delays is used to reconstruct the system. Then, based on the constructed model and by use of the Wirtinger-based inequality technique to deal with the cross items, two less conservative stability and stabilization criteria are derived to enhance the design flexibility. Finally, two numerical examples are used to show the effectiveness of proposed method.