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1.
刘飞  苏宏业等 《控制与决策》2002,17(5):532-535,540
针对T-S模糊模型描述的不确定非线性系统,应用二次D-稳定概念,对给定平面上的某一D域提出模糊系统全局鲁棒D-稳定的充分条件,基于并行分布补偿(PDC)技术,各局部状态反馈镇定控制器设计归结于解一组耦合线性矩阵不等于(LMI),全局控制器通过隶属度函数由各局部控制器混合而成,并以此保证整个系统鲁棒D-稳定性,最后用质量弹簧阻尼系统给出了仿真示例。  相似文献   

2.
利用T-S模型对一类非线性不确定系统进行模糊建模,在此基础上研究模糊鲁棒观测器及模糊状态鲁棒控制器的设计,并证明所设计的模糊鲁棒观测器和模糊状态鲁棒控制器具有全局渐近稳定性质。  相似文献   

3.
一类不确定多输入模糊双线性系统的鲁棒H∞控制   总被引:1,自引:0,他引:1  
针对一类带有参数不确定性和干扰的多输入模糊双线性系统(FBS)的鲁棒H_∞控制问题,使用并行分布补偿算法(PDC)设计了模糊控制器,得到了整个模糊控制系统鲁棒全局稳定的充分条件,控制器的设计可以通过求解一系列线性矩阵不等式(LMI)获得.仿真例子验证了方法的有效性.  相似文献   

4.
Delta算子系统动态输出反馈D-稳定鲁棒协方差控制   总被引:2,自引:0,他引:2  
研究Delta算子描述的线性不确定系统基于动态输出反馈的D-稳定鲁棒协方差控制问题.设计动态输出反馈控制器,使Delta算子不确定系统鲁棒D-稳定,且稳态输出协方差矩阵具有给定上界.利用线性矩阵不等式(LMI)方法,给出D-稳定鲁棒协方差控制器存在的充分条件.在此基础上,提出相应控制器的设计算法.数值算例表明了该设计方法的可行性.  相似文献   

5.
肖民卿  曹长修  姚志强 《控制与决策》2008,23(11):1216-1220

研究Delta 算子描述的线性不确定系统基于动态输出反馈的D- 稳定鲁棒协方差控制问题 .设计动态输出反馈控制器,使 Delta 算子不确定系统鲁棒D- 稳定,且稳态输出协方差矩阵具有给定上界. 利用线性矩阵不等式(LMI)方法,给出D- 稳定鲁棒协方差控制器存在的充分条件. 在此基础上,提出相应控制器的设计算法. 数值算例表明了该设计方法的可行性.

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6.
针对一类多面体不确定线性连续系统,研究了系统鲁棒D-稳定问题.为降低设计的保守性,引入参数相关Lyapunov函数,给出了系统鲁棒D-稳定的充分条件.通过求解一组线性矩阵不等式,得到了系统鲁棒D-稳定的状态反馈控制器,使得闭环系统鲁棒D-稳定.对某卫星姿态控制系统的仿真结果表明了该方法的有效性.  相似文献   

7.
将非线性系统用T-S模糊动态模型描述,并将全局模糊系统模型表示成不确定系统形式采用新的鲁棒控制器设计方法,设计出使全局模糊系统模型渐近稳定的线性控制器避免了并行分配补偿法中求解公共矩阵P的困难.一级倒立摆的模糊控制器设计实例,证明了方案的简洁有效.  相似文献   

8.
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.  相似文献   

9.
王方松  向峥嵘 《控制工程》2004,11(2):165-167
对一类范数有界不确定连续T-S模糊系统,研究了其状态反馈鲁棒方差控制律设计和稳定性问题。利用线性矩阵不等式(LMI)技术,导出了状态反馈鲁棒方差控制律的存在条件和使系统全局渐近稳定的充分条件,并用一组线性矩阵不等式的可行解,给出了使系统全局渐近稳定的状态反馈鲁棒方差控制律的一种参数化表达形式,而这些线性矩阵不等式的解可以用Matlab中LMI工具箱方便地求解。通过对混沌Lorenz系统的仿真,验证了所设计控制器的有效性。  相似文献   

10.
不确定T-S 模型的D -域极点约束鲁棒控制   总被引:3,自引:1,他引:3       下载免费PDF全文
对于具有两类不确定性的Takagi-Sugeno模糊非线性模型,运用二次稳定思想,提出使闭环系统的极点在各种允许的不确定性下始终在复平面上某个二次矩阵不等式区域D中的一个充分条件.基于这一条件和并行分布补偿技术,用线性矩阵不等式方法,设计全局鲁棒D-稳定控制器.最后通过质量弹簧阻尼系统给出了所述设计方法的仿真示例.  相似文献   

11.
竞争式Takagi-Sugeno模糊再励学习   总被引:4,自引:0,他引:4  
针对连续空间的复杂学习任务,提出了一种竞争式Takagi-Sugeno模糊再励学习网络 (CTSFRLN),该网络结构集成了Takagi-Sugeno模糊推理系统和基于动作的评价值函数的再 励学习方法.文中相应提出了两种学习算法,即竞争式Takagi-Sugeno模糊Q-学习算法和竞争 式Takagi-Sugeno模糊优胜学习算法,其把CTSFRLN训练成为一种所谓的Takagi-Sugeno模 糊变结构控制器.以二级倒立摆控制系统为例,仿真研究表明所提出的学习算法在性能上优于 其它的再励学习算法.  相似文献   

12.
The paper deals with the problem of stabilisation of interval systems. To this end, by using Takagi–Sugeno fuzzy mechanism, a Mamdani-type PID-like fuzzy controller is modified and extended to develop a new PID-like Takagi–Sugeno fuzzy stabilising controller for the plant described by an interval system. Indeed, a PID-like Takagi–Sugeno fuzzy controller and an interval plant are considered in the forward path of a unity feedback system, and parameters in Takagi–Sugeno fuzzy controller are determined so that the stability of the closed-loop system is assured. The closed-loop system has a multilinear uncertainty structure. Therefore, based on the Zero Exclusion Condition for multilinear uncertain systems, a new theorem presenting sufficient conditions for the Takagi–Sugeno fuzzy controller to be robust stability guaranteed is also derived. An example is given to illustrate the application and the effectiveness of the proposed controller.  相似文献   

13.
时滞Hopfield神经网络的随机稳定性分析   总被引:1,自引:1,他引:0       下载免费PDF全文
T-S模型提供了一种通过模糊集和模糊推理将复杂的非线性系统表示为线性子模型的方法。研究了时滞Hopfield神经网络的随机稳定性(SFVDHNNs)。首先描述了SFVDHNNs模型,然后用Lyapunov方法研究了SFVDHNNs全局均方指数稳定性,通过可以被一些标准的数值分析方法求解的线性矩阵不等式(LMIs)得出了稳定性标准。  相似文献   

14.
In this paper, we propose some new results on stability for Takagi–Sugeno fuzzy delayed neural networks with a stable learning method. Based on the Lyapunov–Krasovskii approach, for the first time, a new learning method is presented to not only guarantee the exponential stability of Takagi–Sugeno fuzzy neural networks with time-delay, but also reduce the effect of external disturbance to a prescribed attenuation level. The proposed learning method can be obtained by solving a convex optimization problem which is represented in terms of a set of linear matrix inequalities (LMIs). An illustrative example is given to demonstrate the effectiveness of the proposed learning method.  相似文献   

15.
A new approach to fuzzy modeling   总被引:7,自引:0,他引:7  
This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's model (1985), because it has the same structure as that of Takagi and Sugeno's model. It is also as easy to implement as Sugeno and Yasukawa's model (1993) because its identification mimics the simple identification procedure of Sugeno and Yasukawa's model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used, which is a modified version of fuzzy C-means (FCM). In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm  相似文献   

16.
《Applied Soft Computing》2007,7(3):772-782
In this paper a new Takagi–Sugeno (T–S) fuzzy model with nonlinear consequence (TSFMNC) is presented which can approximate a class of smooth nonlinear systems, nonlinear dynamical systems and nonlinear control systems. It is also proved that Takagi–Sugeno fuzzy controller with nonlinear consequence (TSFCNC) can be used to approximate a class of nonlinear state-feedback controllers using the so-called parallel distributed compensation (PDC) method. The inverted pendulum problem has been simulated with TSFCNC and compared with Takagi–Sugeno fuzzy controller with linear consequence (TSFCLC) and the results show that TSFCNC performs better than TSFCLC. A real-life example of dynamic positioning of ship is simulated and the results also show that TSFCNC performs better than TSFCLC.  相似文献   

17.
一种新的复杂系统模糊辨识方法   总被引:5,自引:0,他引:5  
针对一阶Takagi-Sugeno模型辨识复杂系统的困难,提出一种新的模糊模型.这种模 型的结构在一阶Takagi-Sugeno模型的基础上,再进行一次非线性映射.文中运用卡尔曼滤 波算法的模糊神经元网络实现了这种模型.仿真结果表明该方法辨识精度高,且有良好的 实用性.  相似文献   

18.
In this study, a new method is proposed for the exact analytical inverse mapping of Takagi–Sugeno fuzzy systems with singleton and linear consequents where the input variables are described by using strong triangular partitions. These fuzzy systems can be decomposed into several fuzzy subsystems. The output of the fuzzy subsystem results in multi-linear form in singleton consequent case or multi-variate second order polynomial form in linear consequent case. Since there exist explicit analytical formulas for the solutions of first and second order equations, the exact analytical inverse solutions can be obtained for decomposable Takagi–Sugeno fuzzy systems with singleton and linear consequents. In the proposed method, the output of the fuzzy subsystem is represented by using the matrix multiplication form. The parametric inverse definition of the fuzzy subsystem is obtained by using appropriate matrix partitioning with respect to the inversion variable. The inverse mapping of each fuzzy subsystem can then easily be calculated by substituting appropriate parameters of the fuzzy subsystem into this parametric inverse definition. So, it becomes very easy to find the analytical inverse mapping of the overall Takagi–Sugeno fuzzy system by composing inverse mappings of all fuzzy subsystems. The exactness and the effectiveness of the proposed inversion method are demonstrated on trajectory tracking problems by simulations.  相似文献   

19.
The use of multi-objective evolutionary algorithms (MOEAs) to generate a set of fuzzy rule-based systems (FRBSs) with different trade-offs between complexity and accuracy has gained more and more interest in the scientific community. The evolutionary process requires, however, a large number of FRBS generations and evaluations. When we deal with high dimensional datasets, these tasks can be very time-consuming, especially when we generate Takagi–Sugeno FRBSs, thus making a satisfactory exploration of the search space very awkward. In this paper, we first analyze the time complexity for both the generation and the evaluation of Takagi–Sugeno FRBSs. Then we introduce a simple but effective technique for speeding up the identification of the rule consequent parameters, one of the most time-consuming phases in Takagi–Sugeno FRBS generation. Finally, we highlight how the application of this technique produces as a side-effect a decoupling of the rules. This decoupling allows us to avoid re-computing consequent parameters of rules which are not directly modified during the evolutionary process, thus saving a considerable amount of time.In the experimental part we first test the correctness of the predicted asymptotical time complexity. Then we show the benefits in terms of computing time saving and improved search space exploration through an example of multi-objective genetic learning of Takagi–Sugeno FRBSs in the regression domain.  相似文献   

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