一类非线性系统的模糊变结构控制

研究了一类非线性系统的模糊变结构控制问题，并给出了稳定性证明。通过将非线性系统化为多个精确T—S模型来建立非线性系统精确的T—S模糊模型，将模糊理论与成熟的线性变结构控制理论相结合设计一种模糊变结构控制器，用Lyapunov稳定性理论证明该控制器能确保模糊动态模型全局渐近稳定，从而使非线性系统稳定。仿真结果表明了该设计方法的有效性。     

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。     

模糊奇异摄动系统动态输出反馈H∞控制

针对一类非线性奇异摄动系统,建立了基于T-S 模糊模型的模糊奇异摄动系统模型．通过李亚普诺夫 方法和Schur 补定理,研究其动态输出反馈H∞控制．将系统动态输出反馈H∞控制器设计归结为求解一组与摄动参 数e 无关的线性矩阵不等式,避免了由e 引起的数值求解的病态问题．所获得的控制器使闭环系统渐近稳定,并达 到了给定的H∞性能指标．该方法适用于标准和非标准非线性奇异摄动系统．仿真实例说明了该方法的有效性     

使用鲁棒控制器稳定模糊系统模型

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.     

一类不确定时滞系统的模糊滑模控制

利用T-S模糊模型逼近一类非线性不确定时滞系统,将非线性系统模糊化为局部线性系统．基于李亚普诺夫稳定性定理设计出使模糊系统全局稳定的滑模控制器,该控制器对满足匹配条件和不满足匹配条件的不确定性系统均适用．最后以Truck—Trailer模型为例进行仿真研究,其结果验证了设计方案的可行性和有效性．     

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．     

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.     

非线性动态模糊系统过程控制与稳定性分析

针对自动控制领域中存在的大量的非线性动态模糊系统,提出了非线性动态模糊系统过程控制模型,并给出了动态模糊控制器的设计算法和该模型的稳定性分析,很好地解决了模糊控制系统所不能解决的动态性问题.     

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．     

基于广义模糊双曲正切模型的自适应控制器

针对一类非线性函数未知的非线性离散系统，提出一种新的基于广义模糊双曲正切模型的参考模型自适应控制器设计方法，并利用Lyapunov稳定性理论证明了该控制器是全局渐近稳定的．仿真例子证明了该方法的有效性．     

直升机的TSK模糊控制

针对直升机动力学为非线性的特点,且存在不确定因数和状态变化,利用线性控制很难得到好的控制结果,提出利用TSK(Takagi-Sugeno-Kang)模糊系统控制小模型直升机.所设计的TSK模糊控制器是一个基于TSK模糊模型的非线性控制器,能保证闭环控制系统的稳定性.直升机数学模型中线性部分设计状态反馈控制器非线性部分先求TSK模糊模型,然后设计TSK模糊控制器.仿真和实验结果表明,所设计的TSK模糊控制器比线性控制器对直升机控制具有良好的动态响应和稳定性,是一种非常有效的控制方法.     

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     

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.     

基于模糊神经网络的温度控制系统设计

实验室温度控制系统要求精度高,并且具有非线性、大惯性及数学模型难建立等特性,这使得用常规PID控制器以及一般模糊控制器无法很好地满足系统要求,而本文在一般模糊控制器的基础上,融合神经网络技术,设计出模糊神经网络控制器,它既有模糊控制鲁棒性好、动态响应好、上升时间快、超调小的优点,又具有神经网络的在线自学习能力,可以实现温度的智能控制,在实际应用中取得良好的效果。     

An advanced FMRL controller for FACTS devices to enhance dynamic performance of power systems

The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. The used flexible alternating current transmission system (FACTS) in this paper is an advanced super-conducting magnetic energy storage (ASMES). Many control techniques that use ASMES to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES have been actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a fuzzy model reference learning controller (FMRLC) is proposed in this paper, which investigates multi-input multi-output FMRLC for time-variant nonlinear system. This control method provides the motivation for adaptive fuzzy control, where the focus is on the automatic online synthesis and tuning of fuzzy controller parameters (i.e., using online data to continually learn the fuzzy controller that will ensure that the performance objectives are met). Simulation results show that the proposed robust controller is able to work with nonlinear and nonstationary power system (i.e., single machine-infinite bus (SMIB) system), under various fault conditions and disturbances.     

卡车的TSK模糊控制

针对在保证一定精度的条件下,要求一次性完成卡车的倒车的情况,利用TSK (Takagi-Sugeno-Kang)模糊系统控制卡车,在TSK模糊控制器作用下让卡车从任意的初始位置一次性倒车到指定的位置。在卡车数学模型中,线性部分设计线性状态反馈控制器,非线性部分先求TSK模糊模型,然后设计基于TSK模糊模型的TSK模糊控制器。它是一个非线性控制器,可保证闭环系统的稳定性。仿真实验结果表明,所设计的TSK模糊控制器对卡车的倒车控制是非常有效的。     

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。     

State feedback tracking control for indirect field-oriented induction motor using fuzzy approach

This paper deals with the synthesis of fuzzy controller applied to the induction motor with a guaranteed model reference tracking performance. First, the Takagi-Sugeno (T-S) fuzzy model is used to approximate the nonlinear system in the synchronous d-q frame rotating with field-oriented control strategy. Then, a fuzzy state feedback controller is designed to reduce the tracking error by minimizing the disturbance level. The proposed controller is based on a T-S reference model in which the desired trajectory has been specified. The inaccessible rotor flux is estimated by a T-S fuzzy observer. The developed approach for the controller design is based on the synthesis of an augmented fuzzy model which regroups the model of induction machine, fuzzy observer, and reference model. The gains of the observer and controller are obtained by solving a set of linear matrix inequalities (LMIs). Finally, simulation and experimental results are given to show the performance of the observer-based tracking controller.