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.     

基于模糊滑模的不确定混沌系统控制

提出了一种利用直接自适应模糊神经网络控制与模糊滑膜控制相结合来控制一类不确定非线性混沌系统的新方法。应用Takagi-Sugeno模糊逻辑系统设计系统控制律和参数在线调整规则,使控制系统能准确的跟踪给定信号,同时具有较强的抑制系统参数摄动的能力以及抑制随机噪声的能力。仿真实验结果表明,此算法有效地实现了不确定混沌系统的追踪控制,使系统的跟踪误差减小,提高了系统的鲁棒性,应用前景十分广阔。     

基于遗传算法的广义Takagi-Sugeno模糊逻辑系统最优参数辨识

针对Takagi-Sugeno模糊逻辑系统的隶属函数不具有自适应性且模糊规则数的确定 带有很大的人为主观性,这里引入了一类广义Takagi-Sugeno模糊逻辑系统;在模型实现上，以 广义Takagi-Sugeno模型为个体,采用简单、有效的矩阵编码方式,借助遗传算法得到一个次优 的广义Takagi-Sugeno模糊系统模型,该模型不仅能很好地逼近所要辨识的非线性系统,而且 还具有较低的复杂度.仿真结果表明了广义Takagi-Sugeno模型及其参数辨识方法的正确性和 有效性.     

基于T-S模糊模型的混沌系统脉冲控制

给出了一种基于T-S模糊模型的混沌系统模糊脉冲控制方法.首先给出了基于T-S模糊模型对非线性系统精确建模的原理,得到与混沌系统等价的T-S模糊系统.然后根据建模得到的T-S模糊系统,采用模糊脉冲控制技术来实现控制.最后,以控制Ndolschi混沌系统为例,证明了这种方法的有效性.     

基于脉冲同步的混沌系统自适应参数估计

针对一类参数不确定混沌系统,提出了一种基于脉冲同步的自适应参数估计方法,给出了脉冲控制增益和参数自适应律的解析表达式,从理论上证明了该方法可以准确估计出参数不确定混沌系统的所有未知参数.以Lorenz混沌和Ueda振荡器系统为例,数值模拟证明了该方法的有效性.     

一类模型未知系统的辨识和混沌化控制

对于一类模型未知的非混沌系统采用模糊神经网络辨识其动力学特性, 将得到的模糊神经网络辨识模型应用于逆系统方法中, 实现了一类模型未知非混沌系统的混沌化控制. 该方法不依赖于被控对象的数学模型, 就可以进行有效控制. 研究了模糊神经网络辨识误差对控制精度的影响, 证明了适当设计参数可以使由辨识误差引起的控制误差小于辨识误差. 针对连续和离散两类系统的仿真研究证明了该方法的有效性.     

非线性时延网络控制系统的模糊建模与控制

针对时变网络诱导时延小于一个采样周期的非线性时延网络控制系统,讨论系统的稳定性及控制器的设计方法.利用基于“IF-THEN”规则的模糊模型近似系统中的非线性,将时延的不确定性转化为系统参数的不确定性,从而将此类非线性网络控制系统建模为一类具有参数不确定性的离散Takagi-Sugeno（T-S）模糊模型.基于建立的模型,利用Lyapunov方法和线性矩阵不等式方法,分析了系统的稳定性及模糊状态反馈控制器的设计方法,最后通过仿真实例验证了所提出方法的有效性.     

一类离散混沌系统的脉冲时滞反馈控制

为了镇定一类离散混沌系统的不稳定周期轨道,提出脉冲时滞反馈控制方法.系统地研究了脉冲时滞反馈控制方法在一类离散小世界网络模型中的应用问题.数值仿真结果表明,小世界网络模型中的倍周期分岔及其导致的混沌可以被延后,镶嵌在混沌吸引子内部的不稳定周期轨道可以被镇定.与已有相关控制方法相比,所提出的脉冲时滞反馈控制方法具有适用范围广泛、实现过程简单方便且灵活等优势.     

不确定离散时滞模糊系统的广义H2控制

针对一类利用Takagi-Sugeno（T-S）模糊模型,构建了具有状态和输入时滞的不确定离散时间非线性系统,研究了其广义H2稳定和控制器的设计问题。基于分段二次李雅普诺夫（Lyapunov）函数稳定性分析理论,设计出了分段静态输出反馈控制器,使闭环系统对于允许的不确定参数广义H2稳定。数值仿真例子验证了这种控制器的设计方法的有效性和其理论结果的正确性。     

基于模糊模型的时滞不确定系统的模糊H∞鲁棒反馈控制

讨论了一类具有状态和控制时滞的不确定非线性系统的模糊H∞状态反馈控制问题.采用具有时滞的不确定Takagi-Sugeno(T-S)模糊模型对非线性系统进行建模,提出了一套基于LMI的模糊鲁棒控制器的系统设计方法,给出了模糊H∞状态反馈控制器存在的充分条件,以保证闭环模糊系统渐近稳定并满足从干扰输入到控制输出的H∞范数界约束.示例仿真表明了该方法的有效性.     

Robust stability of impulsive Takagi-Sugeno fuzzy systems with parametric uncertainties

This paper presents a kind of time-varying impulsive Takagi-Sugeno (T-S) fuzzy model with parametric uncertainties in which each subsystem of the model is time-varying. Several robust stabilities of time-varying systems with parametric uncertainties, such as general robust stability, robustly asymptotical stability and exponential stability, are studied using uniformly positive definite matrix functions and the Lyapunov method. Specifically, robust stability conditions of time-invariant impulsive T-S fuzzy systems are also derived in the formulation of quasi-linear matrix inequalities (QLMIs) and an iterative LMIs algorithm is designed for solving QLMIs. Finally, a unified chaotic system with continuous periodic switch and a unified time-invariant chaotic system are used for demonstrating the effectiveness of our respective results.     

Robust fuzzy control of nonlinear systems with parametricuncertainties

Addresses the robust fuzzy control problem for nonlinear systems in the presence of parametric uncertainties. The Takagi-Sugeno (T-S) fuzzy model is adopted for fuzzy modeling of the nonlinear system. Two cases of the T-S fuzzy system with parametric uncertainties, both continuous-time and discrete-time cases are considered. In both continuous-time and discrete-time cases, sufficient conditions are derived for robust stabilization in the sense of Lyapunov asymptotic stability, for the T-S fuzzy system with parametric uncertainties. The sufficient conditions are formulated in the format of linear matrix inequalities. The T-S fuzzy model of the chaotic Lorenz system, which has complex nonlinearity, is developed as a test bed. The effectiveness of the proposed controller design methodology is finally demonstrated through numerical simulations on the chaotic Lorenz system     

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning (HVAC) systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno (T-S) fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.     

混沌范德玻—杜芬系统的T—S模糊控制

针对混沌ADVP（范德玻—杜芬）系统,进行了T-S模糊建模和模糊控制器设计,实现了系统的稳定。在用T-S模糊模型精确重构系统结构的基础上,利用反馈同步思想和极点配置方法,基于并行分布补偿（PDC）技术,进行了控制器设计。整个设计过程只需在模糊模型基础上作极点配置,简化了计算,得到了简单且易实现的控制器。仿真表明,受控系统能够快速达到收敛,验证了方法的有效性。     

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.     

不确定参数下离散混沌系统的开闭环控制

在很多实际环境中,系统模型参数经常是不确定的,使离散混沌系统的控制策略可能不再有效。当参数不能直接观测时,开闭环控制方案将由于不精确的系统模型而失效。针对这个问题,提出了参数自适应开闲环控制策略来控制不确定参数的离散混沌系统。对开闲环控制在不确定参数条件下,参数变化满足控制需要的条件进行了讨论。同时,提出了参数自适应开闲环控制,将原有的开闭环控制推广到更大的范围。从Henon系统的仿真中,参数自适应开闲环控制策略显示了良好的抗扰动特性。     

Robust H∞ sliding mode control for uncertain discrete singular T-S fuzzy Markov jump systems

This paper mainly explored the problem of stability in terms of discrete singular T-S fuzzy Markov jump systems with uncertainties and external disturbances. A new sliding mode controller is proposed for this type of system. Firstly, a common sliding surface that can be shared by all subsystems of discrete singular systems is designed, so the jumping effects could be weakened. Secondly, the proof of stochastic admissibility of the closed-loop dynamic system is provided by deriving the linear matrix inequality (LMI) technique. Thirdly, an appropriate sliding mode control (SMC) law is designed to satisfy the discrete reaching condition. And, finally, a simulation is offered to illustrate the effectiveness of the derived results.     

模糊Delta算子系统的鲁棒稳定性分析与控制

研究一类由Delta算子描述的T-S型模糊系统的鲁棒稳定性与状态反馈控制设计问题。基于Delta域的Lyapunov稳定性理论，利用线性矩阵不等式(LMI)的方法，给出了不确定模糊Delta算子系统鲁棒稳定及存在状态反馈控制器使得闭环系统稳定的充分条件。这不仅将连续与离散不确定模糊系统的有关结果纳入Delta算子系统的统一框架中，也为Delta算子方法在非线性系统中的应用研究提供了一种有效途径。数值算例说明了该方法的有效性。