基于模糊逻辑的多机电力系统事件控制

针对电力系统调度监控中采用时间触发控制方式监测电力系统运行的稳定性而导致监测信息繁多,浪费网络资源等问题,提出一种基于模糊逻辑的事件触发控制新方式。本文采用T-S模糊模型对多机电力系统建模,以励磁控制系统作为控制器,运用并行分布补偿技术,在状态可测的情况下,研究了多机电力系统在事件触发控制下的稳定性判据问题。根据李雅普诺夫稳定性理论以及事件触发理论,分析了多机电力系统在李雅普诺夫意义下的稳定性,提出保证该电力系统稳定性的充分条件以及控制器的设计方法,并以线性矩阵不等式形式表示得到的充分条件。以三机电力系统为例验证了在事件触发控制下多机电力系统稳定性判据的可行性。最终采用MATLAB仿真软件对系统进行仿真,从而证明稳定条件的存在。     

基于H∞优化控制的不确定单机无穷大电力系统稳定性研究

针对电力系统在风电并网时由于其随机性及不确定性而产生的波动扰动,致使降低系统稳定性的问题,提出一种基于H_∞优化控制的不确定电力系统稳定性研究方法。通过励磁控制系统利用并行分布补偿技术结合事件触发理论设计模糊控制器,同时采用T-S模糊控制对电力系统进行模糊建模。通过H_∞优化控制理论结合李雅普诺夫稳定性理论研究提出保证单机无穷大电力系统稳定性的充分条件以及系统控制器的设计方法,并将充分条件以线性矩阵不等式形式表示。最后采用MATLAB仿真软件对所求得稳定性判据进行验证,证实该结论的有效性。     

基于H_(∞)优化控制的多机电力系统稳定性研究北大核心

针对提高电力系统静态稳定性从而解决分布式电网并网后产生具有不确定性和偶然性的小干扰问题,本文采用多机电力系统模型的稳定性判据和H_(∞)优化控制问题进行研究,利用T-S模糊模型对多机电力系统模型进行建模,根据李雅普诺夫稳定性理论及H_(∞)优化控制理论,基于线性矩阵不等式(LMI),利用并行分布补偿(PDC)控制器的设计方法,证明了保证多机电力系统稳定的充分条件并实现电力系统的H_(∞)优化控制,给出这类非线性电力系统控制器的设计方法。最后采用MATLAB仿真软件对所求得稳定性判据进行验证,证实该结论的有效性。     

新的基于Type-2模糊系统的直接自适应模糊控制

在Type-1模糊系统的直接自适应控制基础上,将规则前件、后件改为Type-2模糊集合,建立Type-2模糊系统的直接自适应模糊控制器,给出了直接自适应控制器的设计方法,讨论了直接自适应控制系统的稳定性,研究了直接自适应控制系统的收敛性,针对一类非线性系统给出了仿真。     

一类不确定多输入模糊双线性系统的输出反馈控制

针对一类具有范数有界参数不确定性的多输入模糊双线性系统,在系统的状态不完全可测的情况下,提出一种模糊镇定控制方法。设计模糊观测器估计系统的状态,基于模糊观测器,设计模糊控制器保证闭环系统的渐近稳定性。模糊控制器可以通过求解线性矩阵不等式(LMI)求得。仿真示例验证了设计方法的有效性。     

仿生模糊直接自适应控制

针对一类不确定非线性系统,设计了一种具有仿生特性的模糊直接自适应控制方法.方法将反应生物特性的生态位态势理论函数作为模糊规则的后件,将生物个体对外界扰动的适应对策引入控制器设计中,利用超稳定理论设计自适应控制律,保证了闭环系统的稳定性和收敛性.仿真结果表明了方法的有效性,比常规控制方法有更好的控制效果.     

基于观测器的模糊Delta算子系统的控制器设计

对于非线性模糊系统控制器和观测器的分析和设计,提出一种统一方法。利用Delta域离散T—S模糊模型对非线性系统建模,并基于李雅普诺夫稳定性理论给出模糊状态反馈控制器和观测器的设计策略,将所得结果归结为求解一组线性矩阵不等式。同时结论表明:分离性原理对Delta算子T—S模糊系统仍然成立。所得结果可将现有关于连续和离散T—S模糊系统的相关结论统一于Delta算子框架内。     

鲁棒自适应仿生模糊滑模控制北大核心

针对一类非线性系统,将生物个体对外界环境的适应对策弓J入滑模控制中,在模糊滑模控制的基础上提出了仿生模糊滑模控制方法.该法利用生物随外界环境变化的主动适应性来设计控制器.在实际问题中往往出现建模误差、外界干扰等不确定因素,为了补偿模糊控制器与理想控制器的误差,增加了一个鲁棒控制器.这样使系统具有更强的鲁棒性和抗扰动性,有效避免抖振和消除误差,达到理想状态.同时,利用Lyapunov稳定性理论,证明了闭环系统的全局稳定性.最后对线性系统和倒立摆系统进行了仿真,结果表明了方法的有效性和可行性.     

鲁棒自适应仿生模糊滑模控制

针对一类非线性系统,将生物个体对外界环境的适应对策弓J入滑模控制中,在模糊滑模控制的基础上提出了仿生模糊滑模控制方法.该法利用生物随外界环境变化的主动适应性来设计控制器.在实际问题中往往出现建模误差、外界干扰等不确定因素,为了补偿模糊控制器与理想控制器的误差,增加了一个鲁棒控制器.这样使系统具有更强的鲁棒性和抗扰动性,有效避免抖振和消除误差,达到理想状态.同时,利用Lyapunov稳定性理论,证明了闭环系统的全局稳定性.最后对线性系统和倒立摆系统进行了仿真,结果表明了方法的有效性和可行性.     

基于生物态势理论的Backstepping模糊控制

针对一类单输入单输出非线性系统,提出了一种基于生物态势理论的backstepping模糊自适应控制方法.设计中,将系统误差及其导数作为模糊规则的前件,将反应生物特性的生态位态势理论函数作为模糊规则的后件,设计了基于生物态势理论的Backstepping模糊控制器.该控制器将生物个体对外界扰动的适应对策引入设计中,使得控制器的自适应律具有生物自适应特性.并利用Lyapunov方法证明了闭环系统的稳定性.仿真结果进一步验证了方法的有效性.     

Stability analysis of sampled-data fuzzy controller for nonlinear systems based on switching T–S fuzzy model

This paper investigates the system stability of a sampled-data fuzzy-model-based control system, formed by a nonlinear plant and a sampled-data fuzzy controller connected in a closed loop. The sampled-data fuzzy controller has an advantage that it can be implemented using a microcontroller or a digital computer to lower the implementation cost and time. However, discontinuity introduced by the sampling activity complicates the system dynamics and makes the stability analysis difficult compared with the pure continuous-time fuzzy control systems. Moreover, the favourable property of the continuous-time fuzzy control systems which is able to relax the stability analysis result vanishes in the sampled-data fuzzy control systems. A Lyapunov-based approach is employed to derive the LMI-based stability conditions to guarantee the system stability. To facilitate the stability analysis, a switching fuzzy model consisting of some local fuzzy models is employed to represent the nonlinear plant to be controlled. The comparatively less strong nonlinearity of each local fuzzy model eases the satisfaction of the stability conditions. Furthermore, membership functions of both fuzzy model and sampled-data fuzzy controller are considered to alleviate the conservativeness of the stability analysis result. A simulation example is given to illustrate the merits of the proposed approach.     

Adaptive tuning of the fuzzy controller for robots

An adaptive tuning algorithm of the fuzzy controller is developed for a class of serial-link robot arms. The algorithm can on-line tune parameters of premise and consequence parts of fuzzy rules of the fuzzy basis function (FBF) controller. The main part of the fuzzy controller is a fuzzy basis function network to approximate unknown rigid serial-link robot dynamics. Under some mild assumptions, a stability analysis guarantees that both tracking errors and parameter estimate errors are bounded. Moreover, a robust technique is adopted to deal with uncertainties including approximation errors and external disturbances. Simulations of the proposed controller on the PUMA-560 robot arm demonstrate the effectiveness.     

模糊时滞系统的输出反馈控制器设计

利用模糊T-S模型对一类非线性时滞系统进行建模;在此基础上,设计出了模糊静态输出反馈控制器和模糊动态输出反馈控制器,并利用Lyapunov-Razumikhin引理和线性矩阵不等式证明了系统渐近稳定的充分条件,通过求解一系列线性矩阵不等式,得到了反馈增益矩阵。     

Adaptive fuzzy sliding-mode control for multi-input multi-output chaotic systems

This paper describes an adaptive fuzzy sliding-mode control algorithm for controlling unknown or uncertain, multi-input multi-output (MIMO), possibly chaotic, dynamical systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal sliding-mode controller, and the robust controller compensates the difference between the fuzzy controller and the ideal one. The parameters of the fuzzy system, as well as the uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the asymptotic stability and tracking of the controlled system. The effectiveness of the proposed method is shown by applying it to some well-known chaotic systems.     

Stable fuzzy logic design of point-to-point control for mechanical systems

An approach for the development of fuzzy point-to-point control laws for second-order mechanical systems is presented. Asymptotic stability of the resulting closed-loop system is proved using Lyapunov stability theory. Closed-loop performance and robustness are quantified in terms of the parameters of membership functions. As opposed to most existing fuzzy control laws, the closed-loop stability of the proposed controller does not depend on the knowledge of the entire dynamics. Moreover, the approach does not require the plant to be open-loop stable. The proposed approach is demonstrated on design and simulation study of a fuzzy controller for a two-link robotic arm.     

基于模糊双线性模型的非线性系统的跟踪控制

研究一种基于T-S模糊双线性系统的跟踪控制器设计及稳定性分析.使用分布并行补偿法(PDC)设计了模糊控制器,得到模糊双线性系统跟踪控制渐近稳定的充分条件,仿真结果验证了该方法改进了闭环系统的性能.     

模糊输入时滞系统的输出反馈控制及稳定性分析

针对一类状态不可测的模糊输入时滞系统,应用平行分布补偿算法(PDC),设计了模糊观测器,提出了基于模糊观测器的输出反馈控制方法,给出了保证模糊时滞系统渐近稳定的新的充分条件.应用广义Lyapunov函数和线性矩阵不等式方法,证明了模糊输入时滞系统的渐近稳定性,同时给出了控制和观测增益矩阵的分离设计算法.仿真结果进一步验证了所提出的方法和条件的有效性.     

一类不确定非线性系统的执行器故障模糊容错控制

针对一类状态不完全可测的不确定非线性系统,研究了带有执行器故障的容错控制问题.采用 T-S模型对非线性系统进行模糊建模,利用并行分布补偿(PDC)算法设计了状态现潮器和基于状态现 潮器的客错控制,给出了保证该模糊容错控制系统稳定的充分条件.根据李雅普诺夫稳定性理论和线性 矩阵不等式(LMI),证明了所提出的模糊容错控制方法不但使得模糊控制系统渐近稳定,而且能够取得 H∞性能指标.计算机仿真结果进一步验证了所提出方法的正确性.     

Fuzzy control of thyristor-controlled series compensator in power system transients

Power system transient stability is one of the most challenging technical areas in electric power industry. Thyristor-controlled series compensation (TCSC) is expected to improve transient stability and damp power oscillations. TCSC control in power system transients is a nonlinear control problem. This paper presents a T–S-model-based fuzzy control scheme and a systematic design method for the TCSC fuzzy controller. The nonlinear power system containing TCSC is modelled as a fuzzy “blending” of a set of locally linearized models. A linear optimal control is designed for each local linear model. Different control requirements at different stages during power system transients can be considered in deriving the linear control rules. The resulting fuzzy controller is then a fuzzy “blending” of these linear controllers. Quadratic stability of the overall nonlinear controlled system can be checked and ensured using H^∞ control theory. Digital simulation with NETOMAC software has verified that the fuzzy control scheme can improve power system transient stability and damp power swings very quickly.