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.     

一类非线性系统的模糊可靠H_2/H_∞混合控制器设计的LMI方法

基于T-S模糊模型,利用线性矩阵不等式(LMI)方法,研究一类非线性系统的模糊可靠H2/H∞混合控制器设计问题.首先,分别以LMI形式给出了系统模糊可靠H2及H∞控制器存在的充分条件和控制器设计方法;然后.给出系统混合H2/H∞性能的可靠控制器设计方法,所设计的控制器使得故障闭环系统二次稳定,同时满足H∞性能,且优化系统的H2性能指标,最后,通过数值例子验证了结论的正确性和控制器设计方法的有效性.     

T-S模糊系统的稳定性分析与镇定控制器设计

应用广义模糊Lyapunov函数方法研究T-S模糊系统的稳定性与控制器设计问题.首先,将T-S模糊系统表示成模糊广义系统的形式;然后利用广义模糊Lyapunov函数得到模糊系统稳定的充分条件,并且给出基于线性矩阵不等式(LMI)的PDC控制器设计方法.该方法与已有的模糊Lyapunov函数方法相比,计算量小,并且表达成LMI形式,容易求解.最后,通过例子验证方法的优越性和有效性.     

T-S模糊系统的局部稳定

讨论了T-S模糊系统的局部稳定性及控制器设计问题．给出连续T-S模糊系统局部稳定的定义，利用非二次Lyapunov函数和线性矩阵不等式（LMI）方法得到连续T-S模糊系统局部稳定的充分条件，并给出了基于LMI的局部镇定控制器设计方法．该方法不同于已有的全局稳定控制器设计方法，为判别模糊系统的稳定提供了新的选择．最后通过数值算例演示了控制器的设计方法。并证明了该方法的可行性。     

基于参数辨识和T-S模糊模型的一类非线性系统镇定控制器设计

针对一类非线性不确定控制系统,首先采用参数辨识的方法构造出对应的Takagi-Sugeno（T-S）模糊模型;然后运用平行分布补偿（PDC）控制器设计方法进行系统的稳定控制器设计;最终达到镇定原非线性系统的目的.给出一种从T-S模糊模型参数辨识到PDC控制器设计的非线性控制器的设计方法.针对单级倒立摆系统的仿真结果验证了所提出方法的有效性.     

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.     

一类离散切换模糊系统的稳定性

针对离散模糊系统,提出一类离散切换模糊系统的稳定性问题.使用切换技术及单Lyapunov函数、多Lyapunov函数方法构造出连续状态反馈控制器,使得相应的闭环系统渐近稳定,同时设计可以实现系统全局渐近稳定的切换律.模型中的每个切换系统的子系统是离散模糊系统,取常用的平行分布补偿PDC控制器,主要条件以凸组合和矩阵不等式的形式给出,具有较强的可解性.计算机仿真结果表明设计方法的可行性与有效性.     

基于LMI的模糊控制器设计方法

针对Ｔ－Ｓ模糊模型,提出同时满足稳定条件和控制受约束的模糊控制器的线性矩阵不等式设计方法。将稳定性及控制约束转化为满足Ｌｙａｐｕｎｏｖ稳定的凸优化问题,并提出了ＬＭＩ的模糊系统控制器设计和稳定性分析的系统框架。倒立摆的模糊控制器设计实例表明了设计方法的有效性。     

一类区间时变时滞T-S模糊系统的鲁棒控制

针对一类区同时变时滞T-S模糊系统,研究了其时滞相关渐近稳定性以及控制器设计问题.基于Lyapunov稳定性理论和线性矩阵不等式(LMI)工具,并结合自由权矩阵方法,设计一个包含时滞区间均值在内的新Lyapunov-Krasovskii泛函,给出了改进的时滞T-S模糊系统渐近稳定的时滞相关准则.同时,根据并行分布补偿算法,给出了带有记忆的状态反馈模糊控制器的设计方法.最后,实例仿真表明了方法的有效性.     

非线性时变时延系统的模糊采样最优控制

针对非线性时变时延系统,采用输入时延和自由权重矩阵方法研究模糊采样最优控制问题.应用T-S模糊系统表征非线性系统,控制器是零阶保持采样信号.由线性矩阵不等式给出最优控制准则,所设计的模糊采样控制器在闭环系统渐近稳定意义下可保证期望最优控制性能.最后,通过卡车拖车系统实验验证模糊采样控制设计方案的可行性.     

Indirect Adaptive Fuzzy Output Feedback Control with Supervisory Controller for Uncertain Nonlinear Systems

In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be de-activated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method.     

非线性系统的间接自适应模糊输出反馈监督控制

In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be deactivated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method.     

Unmanned bicycle balancing via Lyapunov rule-based fuzzy control

Guaranteed stability fuzzy controller for stabilization the motion of an unmanned bicycle is proposed. First, a fuzzy control system capable of automatically balancing an unmanned bicycle through tracking desired roll angle is developed. Fuzzy logic controller membership functions are defined utilizing scaling factors. To guarantee the stability of the closed loop system, similar to previous approaches reported in the literature, fuzzy If–Then rules are constructed based on Lyapunov stability criterion. It is indicated that the proposed fuzzy controller violates Lyapunov stability criterion. The reason of such a violation is argued in detail. To cope with this shortcoming, some modifications are made to the control strategy to assure stability. Through these modifications, the modified fuzzy controller is developed which simultaneously balances the bicycle and guarantees stability while minimizing roll angle tracking error and its derivative. It is indicated that the improved fuzzy controller can adapt to a variety of initial conditions. Moreover, robustness of the controller against parameter variation is verified through its implementation on different bicycle designs (different sets of bicycle parameters). Simulation results confirm the efficacy of the proposed fuzzy controller in terms of settling time and overshoot in comparison with previous studies. Sensitivity analysis of the controller efficiency with respect to system parameters is also assessed.     

非线性系统的直接自适应输出反馈监督模糊控制

针对一类单输入单输出非线性不确定系统，提出一种稳定的直接自适应模糊输出反馈监督控制算法，该算法不需要系统的状态完全可测的假设条件，监督控制不仅迫使系统的状态在指定的集合内，而且当模糊自适应控制处于良好的工作状态时，监督控制可以关闭，证明了整个模糊自适应输出反馈控制算法可以保证闭环系统稳定。     

基于H∞环路成形和自适应神经模糊推理系统的模糊控制器设计

H_∞环路成形方法设计的控制器阶次较高,不便于工程实现和参数调整;用传统方法确定模糊控制器隶属度函数的参数和模糊规则比较费时且难以保证鲁棒性能和时频域性能指标.针对上述情况,提出了一种综合运用H_∞环路成形和自适应神经模糊推理系统来设计模糊控制器的方法.首先采用H_∞环路成形设计方法,得到鲁棒裕量、动态和稳态性能都符合要求的控制器,然后用自适应神经模糊推理系统来逼近此控制器,最后根据自适应神经模糊推理系统参数确定相应的模糊控制器规则和参数.该方法确定模糊控制器隶属度函数的参数精确而省时,且能保证控制器具有较强的鲁棒性和良好的控制效果.通过对小车倒立摆系统进行的仿真,验证了该控制器设计方法的有效性.     

Design of a hybrid fuzzy logic proportional plus conventionalintegral-derivative controller

Presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral-derivative (fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller, By using the bounded-input/bounded-output “small gain theorem”, the sufficient condition for stability of this controller is derived. Based on the condition, we modify the Ziegler and Nichols' approach to design the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. When a plant can be described by any modeling method, the fuzzy P+ID controller can be determined by an optimization technique. Finally, this controller is used to control a nonlinear system. Numerical simulation results demonstrate the effectiveness of the fuzzy P+ID controller in comparison with the conventional PID controller, especially when the controlled object operates under uncertainty or in the presence of a disturbance     

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.     

A robust stabilization problem of fuzzy control systems and itsapplication to backing up control of a truck-trailer

A robust stabilization problem for fuzzy systems is discussed in accordance with the definition of stability in the sense of Lyapunov. We consider two design problems: nonrobust controller design and robust controller design. The former is a design problem for fuzzy systems with no premise parameter uncertainty. The latter is a design problem for fuzzy systems with premise parameter uncertainty. To realize two design problems, we derive four stability conditions from a basic stability condition proposed by Tanaka and Sugeno: nonrobust condition, weak nonrobust condition, robust condition, and weak robust condition. We introduce concept of robust stability for fuzzy control systems with premise parameter uncertainty from the weak robust condition. To introduce robust stability, admissible region and variation region, which correspond to stability margin in the ordinary control theory, are defined. Furthermore, we develop a control system for backing up a computer simulated truck-trailer which is nonlinear and unstable. By approximating the truck-trailer by a fuzzy system with premise parameter uncertainty and by using concept of robust stability, we design a fuzzy controller which guarantees stability of the control system under a condition. The simulation results show that the designed fuzzy controller smoothly achieves backing up control of the truck-trailer from all initial positions     

模糊逻辑控制系统分析与设计

本文把模糊逻辑控制当作多维继器，用Ｌｐ稳定性和园周判据分析闭环模糊逻辑控制系统的稳定性，给出闭环非线性系统与模糊逻辑控制器结合系统的稳定判据和设计方法，并用计算机仿真试验，进一步将稳定判据应用到不同系统。