离散模糊大系统的分散化PDC 控制器设计: LM I 方法

考虑了由N个相互关联的T—S子模糊系统组成的离散模糊大系统的分散镇定问题，给出了保证该模糊大系统闭环渐近稳定的分散化并行分布补偿(DPDC)控制器的设计方法，这些DPDC控制器的存在条件均以LMI的形式出现，LMI可通过MATLAB的LMI工具箱求解，因此提供了一条综合离散模糊大系统分散化PDC控制器的有效途径，仿真例子说明了所提出方法的有效性。     

T-S模糊随机系统的均方镇定

提出一类基于T-S模糊模型的非线性随机系统均方镇定的线性矩阵不等式(LMI)设计方法．利用非线性随机系统的Lyapunov稳定性理论，导出闭环系统均方稳定的若干LMI条件，并分析了这些条件之间的关系，最后通过数值例子说明了它们的应用．     

改进的T-S 模糊模型鲁棒稳定条件及其在转台控制中的应用

提出了参数不确定T-S 模糊系统的新型鲁棒镇定条件．该条件表示为线性矩阵不等式（LMI）的形式, 将模糊子系统的相互关系收集到一系列矩阵而不是单个矩阵中,因此比近期刊出文献中的宽松二次镇定条件具有更 小的保守性．以该LMI 条件为基础,容易得到不确定T-S 模糊控制系统的状态反馈鲁棒镇定控制器．由于只涉及一 组LMI,控制器设计简单并易于数值求解．在转台这一具有参数不确定性的连续时间非线性系统的控制中,所提方 法的有效性得到成功验证．     

离散T-S模糊系统的部分状态反馈H∞控制

基于部分状态信息的控制器是一类特殊的静态输出反馈控制器,一般难以利用线性矩阵不等式工具求解.本文研究离散T-S模糊系统的部分状态反馈镇定及部分状态反馈H∞控制问题.通过引入松弛变量,将离散T-S模糊系统的部分状态反馈镇定问题转换成求解一组线性矩阵不等式（LMI）,并给出基于LMI的部分状态反馈H∞控制器设计方法.通过数值算例验证了所给方法的有效性.     

T-S模糊系统的局部稳定

讨论了T-S模糊系统的局部稳定性及控制器设计问题．给出连续T-S模糊系统局部稳定的定义，利用非二次Lyapunov函数和线性矩阵不等式（LMI）方法得到连续T-S模糊系统局部稳定的充分条件，并给出了基于LMI的局部镇定控制器设计方法．该方法不同于已有的全局稳定控制器设计方法，为判别模糊系统的稳定提供了新的选择．最后通过数值算例演示了控制器的设计方法。并证明了该方法的可行性。     

基于线性矩阵不等式的参数不确定性关联模糊大系统的分散鲁棒镇定

讨论了参数不确定性关联模糊大系统的分散鲁棒镇定问题,所考虑的参数不确定性满足范数有界条件.基于李雅普诺夫稳定性理论及大系统分散控制理论,采用分散化PDC(parallel distributed compensation)控制器,给出了保证该关联模糊大系统闭环渐近稳定的LMI形式的充分条件,通过MATLAB软件中的LMI工具箱可求解出这些LMI中的控制器参数.仿真例子说明了所提方法的有效性.     

Takagi-Sugeno模糊连续系统的模糊采样控制

研究了T-S模糊连续系统的模糊采样控制问题．利用广义系统的描述方法、Lyapunov-Krasovikii泛函以及线性矩阵不等式(LMI)方法,建立了LMIs形式的依赖于采样时间间隔的模糊采样镇定条件,同时给出了模糊采样控制律的设计方法．所设计的模糊采样控制律可以镇定T-S模糊系统．而且,当连续时间模糊控制律可以镇定T-S模糊系统时,对于足够小的采样时间间隔,带有同样增益矩阵的模糊采样控制律也可以镇定T-S模糊系统．最后,通过两个仿真实例说明了所给方法的有效性．     

离散模糊系统镇定律设计

研究离散型T－S模糊系统镇定问题。针对给出的状态空间模型,基于稳定性判定条件给出了镇定律的直接设计方法,通过求解一组线性矩阵不等式完成模糊系统镇定律的设计。给出的算例说明了该方法的有效性。     

离散模糊系统镇定律设计

研究离散型T-S模糊系统镇定问题.针对给出的状态空间模型,基于稳定性判定条件给出了镇定律的直接设计方法,通过求解一组线性矩阵不等式完成模糊系统镇定律的设计.给出的算例说明了该方法的有效性.     

一种有效的不确定分数阶T-S模糊系统的控制器设计方法

考虑分数阶非线性系统的稳定和镇定问题,基于线性矩阵不等式(LMI)方法,对分数阶T-S模糊系统进行研究.利用并行分布补偿法,设计分数阶T-S模糊系统的控制器.考虑阶次满足$0<\alpha<1$的分数阶系统,给出可以利用Matlab求解的LMI形式的T-S模糊控制器设计镇定判据.该判据的优点是可以处理具有正实部特征根的分数阶T-S模糊系统的稳定性和镇定问题,能够保持与Matignon分数阶系统稳定性结论的一致性,并克服其他方法只能处理特征根在负实部的方法的局限性和保守性.数值仿真结果验证了所提控制器设计方法的有效性.     

Stability analysis and synthesis for an affine fuzzy system via LMIand ILMI: discrete case

This paper develops a stability analysis and controller synthesis methodology for a discrete affine fuzzy system based on the convex optimization techniques. In analysis, the stability condition under which the affine fuzzy system is quadratically stable is derived. Then, the condition Is recast in the formulation of Linear Matrix Inequalities (LMI) and numerically addressed. The emphasis of this paper, however, is on the synthesis of fuzzy controller based on the derived stability condition. In synthesis, the stabilizability condition turns out to be in the formulation of nonconvex matrix inequalities and is solved numerically in an iterative manner. Discrete iterative LMI (ILMI) approach is proposed to obtain the feasible solution for the synthesis of the affine fuzzy system. Finally, the applicability of the suggested methodology is demonstrated via some examples and computer simulations.     

Stability analysis and synthesis for an affine fuzzy control systemvia LMI and ILMI: a continuous case

A new stability analysis and controller synthesis methodology for a continuous affine fuzzy system is proposed in this paper. The method suggested is based on the numerical convex optimization techniques. In analysis, the stability condition under which the affine fuzzy system is quadratically stable is derived and is recast in the formulation of linear matrix inequalities (LMIs). The emphasis of this paper, however, is on the synthesis of fuzzy controller based on the derived stability condition. In the synthesis, the stabilizability condition turns out to be in the formulation of bilinear matrix inequalities and is solved numerically in an iterative manner. Fuzzy local controllers also assume the affine form and their bias terms are solved in a numerical manner simultaneously together with the gains. Continuous iterative LMI (ILMI) approach is presented to obtain a feasible solution for the synthesis of the affine fuzzy system     

Analysis and design of an affine fuzzy system via bilinear matrix inequality

A novel analysis and design method for affine fuzzy systems is proposed. Both continuous-time and discrete-time cases are considered. The quadratic stability and stabilizability conditions of the affine fuzzy systems are derived and they are represented in the formulation of bilinear matrix inequalities (BMIs). Two diffeomorphic state transformations (one is linear and the other is nonlinear) are introduced to convert the plant into more tractable affine form. The conversion makes the stability and stabilizability problems of the affine fuzzy systems convex and makes the problems solvable directly by the convex linear matrix inequality (LMI) technique. The bias terms of the fuzzy controller are solved simultaneously together with the gains. Finally, the applicability of the suggested method is demonstrated via an example and computer simulation.     

具有松弛条件的一类切换模糊系统的稳定性分析

使用切换技术以及多Lyapuriov函数方法．研究一类切换模糊系统的稳定性问题．给出了有连续控制输入时该切换模糊系统的一种松弛稳定性条件，避免了并行分配补偿法中固模糊规则数较多而求解公共矩阵P的困难，同时给出了实现系统全局渐近稳定的切换策略．主要条件以LMI的形式给出，具有较强的可解性．空气调节系统的设计实例表明了所提出设计方法的可行性和有效性．     

时变时滞不确定系统的鲁棒输出反馈控制

研究了时变时滞不确定系统基于状态观测器的动态输出反馈实现鲁棒镇定的分 析和综合问题.所研究的系统不仅同时包含时变状态时滞和时变控制时滞,而且包含时变未 知且有界不确定参数.提出了确保该系统可通过输出反馈鲁棒镇定的充分条件,并将该充分 条件转化为线性矩阵不等式(LMI)问题,最终通过求解两个LMI来构造输出反馈控制律.     

一类线性不确定时滞混杂系统的稳定性

研究了一类具有不确定的线性参数及时滞的混杂系统，利用线性矩阵不等式策略和Riccati方法，给出了系统鲁棒稳定性和可稳定性的充分条件，并运用混杂状态反馈控制策略设计了控制器切换方案．最后的仿真实例表明了控制策略的有效性．     

Robust observer-based control of systems with state perturbations via LMI approach

In this note, the observer-based control for a class of uncertain, linear systems is considered. Exponential stabilizability for the systems is studied and the convergence rate of the system is estimated. A linear matrix inequality (LMI) approach is used to design the observer-based control. The control and observer gains are given from LMI feasible solution. A numerical example is given to illustrate our results.     

Guaranteed Cost Control of Polynomial Fuzzy Systems via a Sum of Squares Approach

This paper presents the guaranteed cost control of polynomial fuzzy systems via a sum of squares (SOS) approach. First, we present a polynomial fuzzy model and controller that are more general representations of the well-known Takagi–Sugeno (T-S) fuzzy model and controller, respectively. Second, we derive a guaranteed cost control design condition based on polynomial Lyapunov functions. Hence, the design approach discussed in this paper is more general than the existing LMI approaches (to T-S fuzzy control system designs) based on quadratic Lyapunov functions. The design condition realizes a guaranteed cost control by minimizing the upper bound of a given performance function. In addition, the design condition in the proposed approach can be represented in terms of SOS and is numerically (partially symbolically) solved via the recent developed SOSTOOLS. To illustrate the validity of the design approach, two design examples are provided. The first example deals with a complicated nonlinear system. The second example presents micro helicopter control. Both the examples show that our approach provides more extensive design results for the existing LMI approach.      

Guaranteed cost control of affine nonlinear systems via partition of unity method

We consider the problem of guaranteed cost control (GCC) of affine nonlinear systems in this paper. Firstly, the general affine nonlinear system with the origin being its equilibrium point is represented as a linear-like structure with state-dependent coefficient matrices. Secondly, partition of unity method is used to approximate the coefficient matrices, as a result of which the original affine nonlinear system is equivalently converted into a linear-like system with modeling error. A GCC law is then synthesized based on the equivalent model in the presence of modeling error under certain error condition. The control law ensures that the system under control is asymptotically stable as well as that a given cost function is upper-bounded. A suboptimal GCC law can be obtained via solving an optimization problem in terms of linear matrix inequality (LMI), in stead of state-dependent Riccati equation (SDRE) or Hamilton–Jacobi equations that are usually required in solving nonlinear optimal control problems. Finally, a numerical example is provided to illustrate the validity of the proposed method.