一类不确定模糊动态时滞系统保成本控制

针对一类不确定非线性动态时滞系统，利用模相T—S模型，通过状态反馈对保成本控制问题进行研究。应用线性矩阵不等式给出模糊闭环系统渐近稳定的充分条件，保证了所有允许的不确定闭环系统是稳定的，而且对于一个给定的二次型成本函数，舱保证闭环成本不超过某个界。仿真结果表明所提出的控制方法是有效的。     

参数不确定T-S模糊交联系统的分散保成本控制

研究了参数不确定T-S模糊交联系统的分散保成本控制问题.对于给定系统所容许的所有不确定参数,设计了分散状态反馈保成本控制器,使得闭环系统不仅渐近稳定而且具有适当的性能指标上界.基于线性矩阵不等式(LM I)处理方法,给出了分散控制器存在的充分条件.仿真算例表明了所给方法的简易、有效.     

不确定时滞系统的保成本控制

针对一类具有时变参数不确定性的时滞系统，结合二次型成本函数研究该系统的保成本控制问题，提出了通过求解参数Ｒｉｃｃａｔｉ矩阵方程正定解的保成本状态反馈控制的设计方法 。     

网络控制系统H∞保成本控制分析

研究了网络控制系统在比例积分输出反馈控制器下的H∞保成本控制问题．针对二次型成本函数，以及传感器与控制器、控制器与执行器之间均存在网络的网络控制系统，利用李亚普诺夫理论和线性矩阵不等式，得到了不仅使闭环系统稳定，而且使成本函数不超过某个确定上界的H∞保成本控制存在的条件；进一步给出了H∞保成本控制的设计方法．最后举例说明该方法的可行性．     

关联模糊大系统的保性能控制器设计:LMI方法

考虑具有参数不确定性的连续时间关联模糊大系统的保性能控制问题.基于LMI方法,给出了该不确定性模糊大系统分散状态反馈保性能控制器的设计方案.所设计的分散状态反馈保性能控制器,不但使得该不确定性模糊大系统闭环渐近稳定,而且使其二次型性能指标的上界最小.     

不确定离散动态系统的保成本控制

不确定离散动态系统的保成本控制俞立（浙江工业大学信息工程学院杭州３１００３２）王景成褚健（浙江大学工业控制技术研究所杭州３１００２７）关键词离散系统，不确定性，保成本控制．１）浙江省自然科学基金资助项目．收稿日期１９９６－０７－２９１引言不确定系统的...     

一类不确定线性系统的混杂状态反馈保成本控制

研究一类不确定线性系统的混杂状态反馈保成本控制问题．系统矩阵和输入矩阵中含有时变不确定性,假设存在有限个备选的控制增益已知的控制器,并且任何单一的状态反馈控制器都不能镇定系统,基于单Lyapunov函数的方法,给出了混杂状态反馈保成本控制的充分条件及切换律的设计方案．当切换系统的控制增益未知时,利用多Lyapunov函数法,给出混杂状态反馈保成本控制的充分条件,并通过仿真验证了该方法的有效性。     

隶属度函数相关的不确定T-S模糊系统的保成本控制

研究了一类带有参数不确定性模糊T-S系统的状态反馈保成本控制问题。通过使用阶梯隶属度函数来近似模糊模型和控制器的隶属度函数,应用Lyapunov稳定性理论,得到了一组新的隶属度函数相关的系统渐近稳定充分条件,该条件可用线性矩阵不等式(LMIs)表示。最后通过仿真验证了该方法的有效性,结果表明,通过取得较小的阶梯宽度,系统可以具有更小的保守性和更好的动态性能。     

一类不确定切换模糊时滞系统的保性能控制

针对一类存在时滞的不确定切换模糊系统,研究系统的保性能控制问题.利用平行分布补偿算法(PDC)给出保性能控制器的设计方法,利用单Lyapunov函数方法和多Lyapunov函数方法,给出不确定切换模糊时滞系统保性能控制器存在的充分条件和切换律设计.使得闭环系统对所有允许的不确定,在所设计的控制器和切换律下实现保性能控制.仿真结果表明方法的有效性.     

基于T-S模型的非线性网络控制系统的量化保成本控制

研究一类非线性网络控制系统的量化保成本控制问题．首先在考虑量化因素的影响下,基于Ｔ-Ｓ模糊模型方法建立包含时延、丢包和量化信息的新的非线性网络控制系统模型;其次运用Ｌｙａｐｕｎｏｖ稳定性理论和并行分布式补偿（ＰＤＣ）方法给出系统稳定性条件,运用锥补方法和线性矩阵不等式（ＬＭＩ）技术求解量化保成本控制器．仿真结果和横向比较结果验证了该方法的有效性．     

基于分段模糊Lyapunov函数的模糊系统稳定性分析和保性能设计

针对一类 Takagi-Sugeno (T-S) 连续模糊系统, 在分析模糊系统前提规则结构信息的基础上, 研究了其稳定性和保性能设计问题. 通过将模糊 Lyapunov 函数 (FLF) 和分段二次 Lyapunov 函数 (PQLF) 结合, 构造出分段模糊 Lyapunov 函数 (PFLF), 并提出了一种新的并行分配补偿 (PDC) 控制器. 基于 PFLF 方法, 得到了线性矩阵不等式 (LMI) 形式的模糊系统分析与设计的求解方法. 该方法继承了 FLF 与 PQLF 的优点. 仿真实例表明: 该方法所得稳定性判据更为宽松, 具有更好的保性能控制效果.     

H2 guaranteed cost fuzzy control design for discrete-time nonlinear systems with parameter uncertainty

This paper presents a design method of H₂ guaranteed cost (GC) fuzzy controllers for discrete-time nonlinear systems with parameter uncertainties. The Takagi and Sugeno (T-S) fuzzy model with parameter uncertainties is employed to represent an uncertain discrete-time nonlinear system. A sufficient condition for the existence of H₂ GC fuzzy controllers is presented in terms of linear matrix inequalities (LMIs). The resulting fuzzy controllers not only guarantee that the closed-loop fuzzy system is quadratically stable, but also provide a guaranteed cost on the H₂ performance index. Furthermore, an optimal H₂ GC fuzzy controller in the sense of minimizing a bound on the guaranteed cost is provided by means of an LMI optimization procedure. Finally, it is also demonstrated, through numerical simulations on the backing up control of a truck-trailer, that the proposed design method is effective.     

Guaranteed cost control for T-S fuzzy systems with time-varying delays

This paper deals with the problem of guaranteed cost control for nonlinear systems with time-varying delays which is represented by Takagi-Sugeno (T-S) fuzzy models with time-varying delays. The derivatives of timevarying delay are not necessary to be bounded. Based on the free weighting matrix method, sufficient conditions for the existence of fuzzy guaranteed cost controller via state feedback are given in terms of linear matrix inequalities (LMIs). A minimizing method is also proposed to search the suboptimal upper bound of the guaranteed cost function. The results are delay-dependent but contain delay-independent criteria as a special case. A numerical example is presented to demonstrate the effectiveness and less conservativeness of our work.     

Robust guaranteed cost control of uncertain fuzzy systems under time-varying sampling

In practice, the system is often modeled as a continuous-time fuzzy system, while the control input is applied only at discrete instants. This system is called a sampled-data control system. In this paper, robust guaranteed cost control for uncertain sampled-data fuzzy systems is discussed. A guaranteed cost control where a quadratic cost function is bounded by a certain scalar, not only stabilizes a system but also considers a control performance. A typical sampled-data control is the zero-order input, which can be represented as a piecewise-continuous delay. Here we take a delay system approach to the sampled-data guaranteed cost control problem. The closed-loop system with a sampled-data state feedback controller becomes a system with time-varying delay. First, guaranteed cost control performance conditions for the closed-loop system are given in terms of linear matrix inequalities (LMIs). Such conditions are derived by using Leibniz–Newton formula and free weighting matrix method for fuzzy systems under the assumption that sampling time is not greater than some prescribed scalar. Then, a design method of robust guaranteed cost state feedback controller for uncertain sampled-data fuzzy systems is proposed. Examples are given to illustrate our robust sampled-data guaranteed cost control design.     

基于T-S模型的倒立摆最优保性能模糊控制

对一类具有范数有界参数不确定性T-S模糊模型系统,采用状态反馈的并行分布补偿器(PDC)结构,基于线性矩阵不等式处理方法,研究了其最优保性能模糊控制律的设计问题.导出了保性能模糊控制律存在的条件,通过求解一个凸优化问题给出了最优保性能模糊控制律的设计方法,并用此方法设计了倒立摆系统的最优保性能模糊控制器.仿真实验验证了该设计方法的有效性.     

一类不确定离散非线性时滞系统保性能控制

针对状态不完全可测的一类不确定连续离散时间非线性系统,基于状态观测器研究其保性能控制问题。采用T-S模型对非线性系统进行建模,根据李雅普诺夫稳定性理论,基于线性矩阵不等式(LMI),利用分散化并行分布补偿(PDC)的方法设计了基于观测器的控制器,给出了实现该非线性系统保性能控制的充分条件。在此基础上通过求解相应的线性矩阵不等式组给出了控制器和观测器的设计方法,并通过数值仿真验证了提出的方法,仿真结果表明该方法是正确的。     

Intelligent digital redesign for nonlinear systems using a guaranteed cost control method

In this paper, a novel intelligent digital redesign (IDR) technique using the guaranteed cost control method is proposed for nonlinear systems which can be represented by a Takagi-Sugeno (T-S) fuzzy model. The IDR technique, which is one of the sampled-data fuzzy controller design methods, guarantees not only the stability condition of the sampled-data closed-loop system with the sampleddata fuzzy controller and the state-matching error is presented. By using the concept of the guaranteed cost control method, sufficient conditions are obtained for both minimization of the state-matching error and stabilization of the sampled-data closed-loop system and derived in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to verify the effectiveness of the proposed technique.     

Non-fragile guaranteed cost fuzzy control for nonlinear first-order hyperbolic partial differential equation systems

This paper concerns the non-fragile guaranteed cost control for nonlinear first-order hyperbolic partial differential equations (PDEs), and the case of hyperbolic PDE systems with parameter uncertainties is also addressed. A Takagi–Sugeno (T–S) fuzzy hyperbolic PDE model is presented to exactly represent the nonlinear hyperbolic PDE system. Then, the state-feedback non-fragile controller distributed in space is designed by the parallel distributed compensation (PDC) method, and some sufficient conditions are derived in terms of spatial differential linear matrix inequalities (SDLMIs) such that the T–S fuzzy hyperbolic PDE system is asymptotically stable and the cost function keeps an upper bound. Moreover, for the nonlinear hyperbolic PDE system with parameter uncertainties, using the above-design approach, the robust non-fragile guaranteed cost control scheme is obtained. Furthermore, the finite-difference method is employed to solve the SDLMIs. Finally, a nonlinear hyperbolic PDE system is presented to illustrate the effectiveness and advantage of the developed design methodology.     

Guaranteed‐cost fuzzy controller design for a self‐sustaining bicycle with practical constraints

This study presents a guaranteed‐cost fuzzy controller for a self‐sustaining bicycle. First, the nonlinear dynamics of the bicycle are exactly transformed into a T‐S fuzzy system with model uncertainty. The guaranteed‐cost fuzzy controller is then designed for the transformed T‐S fuzzy system. For practical considerations, the input/state constraints are also satisfied in the design. The main contribution of this study is the guaranteed‐cost control design for a T‐S fuzzy system with model uncertainty and input/state constraints. Finally, simulation results show the validity of the proposed controller design method. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society