基于T-S模型的体操机器人系统模糊变结构控制

针对体操机器人（Acrobot）这类非线性系统，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模型建模,得到Acrobot的全局模糊模型;然后基于Lyapunov理论设计出保证Acrobot全局渐近稳定的模糊变结构平衡控制器．仿真结果表明，所设计的模糊变结构控制器与普通变结构控制器相比．可使Acrobot系统在垂直向上平衡点附近具有更大的吸引域和更强的鲁棒性．     

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。     

一种模糊滑模变结构控制方案及其在混沌系统控制中的应用

采用模糊动态模型逼近非线性系统，将非线性 系统模糊化为局部线性模型．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．应用到两类混沌系统的稳定控制中，验证了方案的有效性．模糊控制器 简单，规则少．     

一类不确定时滞模糊系统的鲁棒H∞控制

对于一类不确定非线性时滞系统,研究使系统二次稳定的状态反馈控制方法。利用T—S模糊模型对时变时滞不确定非线性系统进行建模,采取分段光滑（PSQ）的Lyapunov函数和线性矩阵不等式方法给出使系统二次稳定的模糊状态反馈控制器存在的充分条件,避免并行补偿法中求解公共矩阵P的困难。仿真试验证明,通过该方法设计的控制器具有良好的鲁棒性,控制效果良好。     

T-S 模糊时滞模型的时滞相关稳定性分析和镇定

研究一类用T—S模糊模型描述的非线性不确定时滞系统的时滞相关鲁棒镇定问题．基于线性矩阵不等式的可行解，首先给出利用T—S模糊模型描述的非线性时滞系统时滞相关稳定性准则；然后给出了经状态反馈鲁棒镇定设计的新方法．所设计的控制器能确保闭环系统渐近稳定．     

基于状态观测器的一类非线性系统的模糊鲁棒控制

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。     

基于T-S模型的非线性系统的最终滑动模态控制

采用T_S模糊动态模型逼近非线性系统, 将非线性系统模糊化为局部线性模型. 用Lyapunov稳定性理论设计出确保T-S模型全局渐近稳定的变结构控制器. 采用单位向量控制形式的最终滑动模态控制器, 对满足匹配条件和不满足匹配条件的不确定性均适用. 以倒立摆为模型的仿真实验, 验证了方案的有效性.     

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.     

不确定Duffing混沌系统的同步控制

在模糊滑模变结构控制基础上,研究具有不确定性Duffing混沌系统的同步控制问题。选择合适的滑模面,基于Lyapunov稳定性理论设计模糊滑模变结构控制器及自适应更新规则,从理论上证明控制系统的稳定性。由于控制器的设计是基于自适应模糊滑模变结构控制的,与常规方法相比,控制器滑动模态不受干扰的影响,有较好的鲁棒性和快速跟踪能力。通过数值仿真实验验证了该系统的有效性。     

基于T-S模型的内模控制算法及其应用

针对模糊内模控制算法中模型的建立及模型求逆困难的问题，对一种模糊建模方法进行了改进，在此基础上提出了一种基于T—S模型的内模控制方法。采用启发性知识与复合非线性优化方法相结合的综合方法求解出模糊模型的结构，由模糊辨识获得过程的T—S模型和逆模型，并以此为基础建立内模控制算法。将该算法分别应用于慢时变非线性对象和具有大时延大惯性的热工系统主蒸汽温度的控制，仿真结果表明了该方法具有结构简单，计算效率高等优点，有利于在线应用。     

Variable-Gain Controllers for Nonlinear Systems Using the T–S Fuzzy Model

This correspondence proposes two novel control schemes with variable state-feedback gain to stabilize a Takagi-Sugeno (T-S) fuzzy system. The T-S fuzzy model is expressed as a linear plant with nonlinear disturbance terms in both schemes. In controller I, the T-S fuzzy model is expressed as a linear plant around a nominal plant arbitrarily selected from the set of linear subsystems that the T-S fuzzy model consists of. The variable gain then becomes a function of a gain parameter that is computed to neutralize the effect of disturbance term, which is, in essence, the deviation of the actual system dynamics from the nominal plant as the system traverses a specific trajectory. This controller is shown to stabilize the T-S fuzzy model. In controller II, individual linear subsystems are locally stabilized. Fuzzy blending of individual control actions is shown to make the T-S fuzzy system Lyapunov stable. Although applicability of both control schemes depends on the norm bound of unmatched state disturbance, this constraint is relaxed further in controller II. The efficacy of controllers I and II has been tested on two nonlinear systems     

基于T-S模型的模糊神经网络PID控制

针对在非线性、时变不确定系统中，常规PID控制器难以获得满意效果的问题，仿照传统PID控制器结构，设计了一种基于T-S模型的模糊神经网络PID控制器。该控制器基于T-S模糊模型，将PID结构融入模糊控制中，充分发挥了模糊系统非线性、可解释性的特点；然后又利用神经网络的学习算法，实现了对模糊控制器的参数调整，使控制器具有了适应时变、不确定系统的自学习和自组织能力。针对非线性、时变系统，将此控制器与传统PID控制器对比进行了仿真研究，并应用于啤酒发酵领域，其结果表明，该控制器取得了令人满意的效果。     

Variable-gain controllers for nonlinear systems using the T-S fuzzy model.

This correspondence proposes two novel control schemes with variable state-feedback gain to stabilize a Takagi-Sugeno (T-S) fuzzy system. The T-S fuzzy model is expressed as a linear plant with nonlinear disturbance terms in both schemes. In controller I, the T-S fuzzy model is expressed as a linear plant around a nominal plant arbitrarily selected from the set of linear subsystems that the T-S fuzzy model consists of. The variable gain then becomes a function of a gain parameter that is computed to neutralize the effect of disturbance term, which is, in essence, the deviation of the actual system dynamics from the nominal plant as the system traverses a specific trajectory. This controller is shown to stabilize the T-S fuzzy model. In controller II, individual linear subsystems are locally stabilized. Fuzzy blending of individual control actions is shown to make the T-S fuzzy system Lyapunov stable. Although applicability of both control schemes depends on the norm bound of unmatched state disturbance, this constraint is relaxed further in controller II. The efficacy of controllers I and II has been tested on two nonlinear systems.     

一种基于T-S模糊模型的混沌控制方法

用T-S模糊系统来逼近非线性系统,它的IF-THEN规则后件由线性状态空间子系统构成,进而可以应用模糊系统的控制理论求得模糊控制器,用此非线性控制器来控制非线性系统,以求良好的控制效果;将模糊控制技术应用于混沌控制中,可以克服反馈线性化等传统方法对参数完全精确已知的限制;模糊规则后件部分以局部线性方程形式给出的T-S模糊模型可以通过调整相关参数很好地逼近混沌系统,基于该模型采用平行分散补偿技术设计出具有相同规则数目的模糊控制器,控制器所有参数可以通过求解一组线性矩阵不等式一次性得到。仿真结果验证了该方法的有效性。     

Chattering-free fuzzy variable structure control for multivariable nonlinear systems

In this paper, a fuzzy logic controller (FLC) based variable structure control (VSC) with guaranteed stability for multivariable systems is presented. It is aimed at obtaining an improved performance of nonlinear multivariable systems. The main contribution of this work is firstly developing a generic matrix formulation of the FLC-VSC algorithm for nonlinear multivariable systems, with a special attention to non-zero final state. Secondly, ensuring the global stability of the controlled system. The multivariable nonlinear system is represented by T-S fuzzy model. The identification of the T-S model parameters has been improved using the well known weighting parameters approach to optimize local and global approximation and modeling capability of T-S fuzzy model. The main problem encountered is that T-S identification method cannot be applied when the membership functions (MFs) are overlapped by pairs. This in turn restricts the application of the T-S method because this type of membership function has been widely used in control applications. In order to overcome the chattering problem a switching function is added as an additional fuzzy variable and will be introduced in the premise part of the fuzzy rules together with the state variables. A two-link robot system and a mixing thermal system are chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of proposed FLC-VSC method.     

Improved L2 Gain Performance Controller Synthesis for Takagi–Sugeno Fuzzy System

In this paper, an improved L2 gain performance controller synthesis is proposed for Takagi-Sugeno (T-S) fuzzy system. The T-S fuzzy controller can be easily derived by a three-step procedure with the linear matrix inequalities (LMIs) technique. First, a new T-S fuzzy model structure is presented, which includes the original T-S fuzzy plant with stable pre- and post filters on the input and output of the original plant. Second, by using this structure the L2 gain performance controller design problem can be easily transformed into standard LMIs formulation. Compared with the previous results, it not only gives us a simple structure of the T-S fuzzy controller, but also provides us effective LMIs-based conditions, which include a small number of unknown matrix variables; consequently, less value of L2 gain performance of the closed-loop system can be obtained. Third, an augmented T-S fuzzy controller which guarantees L2 gain performance is obtained for the original T-S fuzzy plant, which is composed of the T-S controller derived from step two and two stable pre- and post filters. Finally, some numerical examples are demonstrated to show the effectiveness of the proposals.     

非线性模糊时滞系统鲁棒自适应控制

研究一类基于模糊T-S模型的非线性时滞系统鲁棒镇定问题．基于记忆型状态反馈策略，首先给出由T-S模糊模型描述的非线性时滞系统在时滞精确已知情况下的鲁棒镇定准则；然后给出非线性时滞系统在时滞未知情况下的鲁棒自适应控制策略．所设计的控制器可确保闭环系统渐近稳定，且具有良好的可操作性．最后通过仿真实例证明了该方法的正确性和有效性．