电弧炉电极调节系统 Fuzzy-PD 控制器的仿真研究

该文介绍了采用MATLABV5 .2提供的模糊逻辑工具箱来设计研究电弧炉电极调节系统中的模糊 -PD控制器 ,讨论了在SIMULINK环境下模糊 -PD控制器的参数自调整原理、结构、建立模糊控制规则库和模糊推理方法 ,并给出了仿真结果与结论。     

一类离散非线性不确定互联系统的模糊分散控制

利用模糊控制方法研究一类离散非线性互联系统的分散控制问题.首先采用模糊(T-S)模型对离散非线性不确定互联系统进行模糊建模,应用并行分布补偿算法(PDC)给出状态反馈分散模糊控制方案,并基于李亚普诺夫函数方法证明了闭环系统的稳定性.然后当系统的状态不完全可测时,设计模糊分散观测器来估计各子系统的状态,从而给出基于观测器的状态反馈分散模糊控制设计的方法.因为该分散模糊控制设计问题是以线性矩阵不等式的形式给出,所以很容易用凸优化方法求解.仿真结果验证了所提出控制方法的有效性.     

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。     

室内热舒适环境的模糊关系矩阵模型控制系统

针对复杂非线性系统中多个输出不能解耦的控制问题,基于实际系统的采样数据,利用矩阵半张量积运算建立模糊关系矩阵模型,得出一种新型模糊控制器设计方法.将传统模糊控制中的推理过程和模糊规则的建立转化成矩阵运算,有效避免了建模和控制的复杂性.将其应用于室内热环境舒适度控制系统中,基于室内外的温度、湿度等多个变量的采样数据对,给出具体的模糊关系矩阵的建立过程,设计得出新型模糊控制器.仿真和实验结果表明,该方法能够实现室内热环境的舒适度控制,并且不需要进行多变量之间的解耦.     

离散模糊系统分析与设计的模糊Lyapunov方法

研究离散T-S模糊控制系统基于模糊Lyapunov函数的稳定性分析及控制器设计问 题.首先,构造出离散型模糊Lyapunov函数,模糊Lyapunov函数是系数与T-S模糊系统的模糊 规则权重相对应的复合型Lyapunov函数.然后,得到了开环系统新的稳定性充分条件,与公共 Lyapunov方法的结果相比,这一条件更为宽松.进而,基于一系列线性矩阵不等式设计出模糊 控制器.最后,仿真实例说明了该方法的算法和本文条件的优越性.     

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.     

最优模糊控制器的系统设计

典型模糊控制器在难于建立数学模型或对控制精度要求不高的场合已得到了成功的应用，但与传统的控制方法相比，它存在两个明显的弱点，一个所得到的模糊控制规则不能保证其是优的或是次最优的，二是在设计模糊控制器获取模糊控制规则时主要是凭经验进行，没有的步骤可遵循，本文提出了一类最优模糊控制器的系统设计方法及步骤，这种最优模糊控制器既有典型模糊控制器的形式，又有类似于线性二次型的性能指标Ｊ＝ｒｆ∑ｋ＝１（ａＮＡ     

一种基于遗传算法的模糊控制规则的优化方法

模糊控制规则的选择是模糊控制器设计的关键问题之一,文中在对现有应用遗传算法优化模糊控制规则的方法进行研究的基础上,以模糊控制规则的完整性和一致性为出发点,提出了一种用遗传算法来优化模糊控制规则的改进算法,具体给出了遗传算法设计中的各种函数和算子的确定,并将优化过的规则用于设计模糊控制器,进行仿真研究,取得了令人满意的效果。     

模糊控制规则优化方法研究

模糊控制规则的选择是模糊控制器设计的关键问题之一,在现有应用遗传算法优化模糊控制规则的方法进行研究的基础上,以模糊控制规则的完整性和一致性为出发点,提出了一种用遗传算法来优化模糊控制规则的改进算法,具体给出了遗传算法设计中的各种函数和算子的确定,并将优化过的规则用于设计模糊控制器,进行仿真研究,取得了令人满意的效果。     

模糊系统H∞控制器设计的LMI方法

应用LMI(线性矩阵不等式)方法,研究了T-S模糊系统H∞控制器的设计问题.首先给出了T-S模糊系统基于状态反馈H∞控制存在的两个新的充分条件.新条件不但简洁而且把模糊子系统间的相互作用表示为由子系统的系数矩阵构成的矩阵不等式.然后新条件被转化为可直接应用Matlab求解的线性矩阵不等式.最后应用线性矩阵不等式方法和Matlab,给出了T-S模糊系统H∞控制器的设计方法.     

Multi-variable fuzzy logic control for a class of distributed parameter systems

A multi-variable fuzzy logic controller (FLC) is proposed to control a class of distributed parameter systems (DPSs). When a DPS is transformed into finite-dimensional ordinary differential equations (ODEs) by using time/space separation, each ODE can be considered as a subsystem. According to design strategy of conventional FLC, one FLC should be designed for one subsystem. It will be very complex because there are many subsystems. In order to reduce design complexity, only a MF and a rule base are designed in the controller. For other subsystems or ODEs, their MFs can be designed equivalently by introducing scaling factors. Then, the proposed FLC has ability to control multi-variable processes. At last, the proposed FLC is applied to control a rod catalytic reaction process. The simulation results demonstrate the effectiveness of the proposed fuzzy control strategy.     

基于遗传算法和模糊逻辑的智能飞控技术研究

设计了基于遗传算法和模糊逻辑控制的智能飞行控制系统及采用论域自调整的模糊控制器，控制器以角度跟踪误差及其微分信号为输入来控制相应的气动舵面偏转，实现对该姿态的跟踪控制。文中给出了控制器输入输出的隶属函数，设计了相应的规则库。并在此基础上进一步利用遗传算法对模糊控制器进行优化设计，给出了遗传算法各个参数的选择原则。仿真结果表明，基于遗传算法和模糊逻辑的智能飞控系统具有良好的控制效果。     

A Genetic Approach for Simultaneous Design of Membership Functions and Fuzzy Control Rules

Based on the genetic algorithm (GA), an approach is proposed for simultaneous design of membership functions and fuzzy control rules since these two components are interdependent in designing a fuzzy logic controller (FLC). With triangular membership functions, the left and right widths of these functions, the locations of their peaks, and the fuzzy control rules corresponding to every possible combination of input linguistic variables are chosen as parameters to be optimized. By using a proportional scaling method, these parameters are then transformed into real-coded chromosomes, over which the offspring are generated by rank-based reproduction, convex crossover, and nonuniform mutation. Meanwhile, the concept of enlarged sampling space is used to expedite the convergence of the evolutionary process. To show the feasibility and validity of the proposed method, a cart-centering example will be given. The simulation results will show that the designed FLC can drive the cart system from any given initial state to the desired final state even when the cart mass varies within a wide range.     

A self-organizing fuzzy logic controller for the active control of flexible structures using piezoelectric actuators

This paper proposes an on-line self-organizing fuzzy logic controller (FLC) design applied to the control of vibrations in flexible structures containing distributed piezoelectric actuator patches. In this methodology, the fuzzy rules are generated using the history of input/output (I/O) pairs without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. The validity of the proposed fuzzy logic control has been demonstrated experimentally in a steel cantilever test beam and a set of experimental tests are made in the system to verify the efficiency of the on-line self-organizing fuzzy controller.     

Design of interval type-2 fuzzy sliding-mode controller

In this paper, an interval type-2 fuzzy sliding-mode controller (IT2FSMC) is proposed for linear and nonlinear systems. The proposed IT2FSMC is a combination of the interval type-2 fuzzy logic control (IT2FLC) and the sliding-mode control (SMC) which inherits the benefits of these two methods. The objective of the controller is to allow the system to move to the sliding surface and remain in on it so as to ensure the asymptotic stability of the closed-loop system. The Lyapunov stability method is adopted to verify the stability of the interval type-2 fuzzy sliding-mode controller system. The design procedure of the IT2FSMC is explored in detail. A typical second order linear interval system with 50% parameter variations, an inverted pendulum with variation of pole characteristics, and a Duffing forced oscillation with uncertainty and disturbance are adopted to illustrate the validity of the proposed method. The simulation results show that the IT2FSMC achieves the best tracking performance in comparison with the type-1 Fuzzy logic controller (T1FLC), the IT2FLC, and the type-1 fuzzy sliding-mode controller (T1FSMC).     

发电机组汽门系统的多模型自学习控制

针对发电机组的非线性、大范围运行等实际问题,研究了用于汽门系统的多模型自学习控制（MMSC）,首先根据各种工况下的样本数据归纳出模糊控制规则;然后由模糊聚类算法将多种工况约简为典型工况,得到相应的子模型模糊控制器（FLC）．以子模型FLC输出的加权集成作为MMSC的控制输出,而加权系数取决干子模型匹配度．在子模型FLC学习优化中,由支持向量机离线逼近模糊规则曲面,再由梯度下降算法在线自学习．仿真实验验证了所设计控制器的优良性能．     

Approximated fuzzy logic controlled shunt active power filter for improved power quality

Shunt active power filters have been widely used for power quality improvement. With the advancement in artificial intelligence techniques, the applications of fuzzy logic‐based control systems have increased manifolds. This paper proposes a reduced rule fuzzy logic controller (FLC) in the voltage control loop of a shunt active power filter (APF), which is approximating a conventional large rule FLC. The difference between the controlled outputs of two controllers is compensated by proposed compensating factors. The dynamic response and harmonic compensation performance of proposed 4‐rule approximated fuzzy logic controller (AFLC) is compared with 25‐rule FLC. A three‐phase shunt APF is used for harmonic and reactive power compensation. The proposed scheme is tested with randomly varying single and multiple non‐linear loads. The simulation results presented under transient and steady‐state conditions confirm that the proposed 4‐rule AFLC efficiently approximates the 25‐rule FLC. The proposed control methodology takes less computational time and computational memory as the numbers of rules are reduced significantly.     

发电机组的一种多模型自学习控制

针对发电机组励磁与汽门的综合控制,研究了一种多模型自学习控制(MMSC).首先,建立机组不同工况下的样本数据并归纳模糊控制器(FLC)规则,随后采用模糊聚类算法将样本约简为典型工况,并得到对应于典型工况的模型库与控制器库.MMSC的控制量为多个FLC输出的加权集成,而加权系数由模型匹配程度决定.采用学习能力强的支持向量机来实现FLC的自学习和在线优化.仿真实验验证了MMSC的控制性能和效果.     

Fault tolerant control based on neural network interval type-2 fuzzy sliding mode controller for octorotor UAV

In this paper, a robust controller for a six degrees of freedom (6 DOF) octorotor helicopter control is proposed in presence of actuator and sensor faults. Neural networks (NN), interval type-2 fuzzy logic control (IT2FLC) approach and sliding mode control (SMC) technique are used to design a controller, named fault tolerant neural network interval type-2 fuzzy sliding mode controller (FTNNIT2FSMC), for each subsystem of the octorotor helicopter. The proposed control scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the number of rules for the fuzzy controller, and guaranteeing the stability and the robustness of the system. The simulation results show that the FTNNIT2FSMC can greatly alleviate the chattering effect, tracking well in presence of actuator and sensor faults.     

质子交换膜燃料电池动态建模及其双模控制

由于已提出的质子交换膜燃料电池(PEMFC)模型难于控制, 提出利用MATLAB/SIMULINK仿真工具进行PEMFC系统动态建模, 同时为实现对PEMFC系统输出电压的控制, 采用了基于模糊规则切换的模糊逻辑控制器(FLC)和比例积分微分控制器(PID)相结合的双模控制方式. 仿真结果证明该动态模型易于控制, 能够反映出PEMFC系统的动态输出特性, 而且验证了基于模糊规则切换的双模控制能够有效抑制扰动, 改善PEMFC系统的动态输出特性, 保证系统的稳定运行, 有助于对PEMFC系统的输出性能分析以及实时控制系统的设计.