一种通用模糊控制器的研究与设计

设计了一种通用模糊控制器，可适用于不同控制对象。采用软件的方法在线调整量化因子及比例因子，并在控制过程中对规则自动调整和完善，从而使控制规则趋于最优。仿真结果表明，这种通用模糊控制器的控制性能优于普通的模糊控制器，达到较高的控制精度。     

量化因子对二级倒立摆模糊控制器性能的影响

本文研究了模糊控制器量化因子对二级倒立摆模糊控制器性能的影响,提出了选择量化因子的一些基本方法、准则.通过比较、分析,以及对二级倒立摆系统的成功控制,可知本文提出的方法、准则是合理的.本文最后给出了在本文方法确定的量化因子作用下的模糊控制器与某文献模糊控制算法控制效果的比较,仿真结果显示了本文方法的有效性.     

基于演化计算的自校正模糊控制自动舵研究

本文提出了一种自校正模糊控制自动舵,该自 动舵采用模糊控制器对航向进行控制,通过演化计算对模糊控制器的加权因子和量化因子进 行调整．仿真证明,该算法与普通模糊控制器相比具有较好的控制性能．     

基于GA的模糊控制器在PWM直流位置控制系统中的应用

设计了一种模糊控制器。其中，模糊控制器输入量和输出量的隶属函数以及量化因子用遗传算法进行优化，并把该控制器用于一个基于PWM技术的直流位置控制系统。与原来采用的PI控制器相比，模糊控制器对系统参数的变化适应性更强，控制效果更好。     

基于改进量子粒子群算法的NCS模糊控制器参数优化

针对网络化控制系统中模糊控制器的量化因子和比例因子采用传统经验方法难以整定的问题, 提出了一种改进量子粒子群（IQPSO）算法对模糊控制器量化因子和比例因子进行优化。该方法将ABC算法中的搜索算子作为变异算子引入到QPSO算法中, 使得IQPSO算法较好地克服了QPSO算法保持种群多样性差容易早熟收敛的缺陷, 并以ITAE指标作为IQPSO算法的适应度函数对模糊控制器进行优化。典型工业过程仿真结果表明, IQPSO优化的模糊控制器具有比PID控制器和标准QPSO优化的模糊控制器更好的控制性能和适用性。     

参数自调整模糊控制器在空调器中的应用

针对空调系统的复杂情况,介绍了一种在线调整常规模糊控制器的量化因子和比例因子的设计方法,实时对常规模糊控制器进行优化。仿真表明,其动态性能和稳态性能都优于常规模糊控制器,具有良好的控制性能。     

基于改进量子粒子群算法的NCS模糊控制器参数优化

针对网络化控制系统中模糊控制器的量化因子和比例因子采用传统经验方法难以整定的问题,提出了一种改进量子粒子群(IQPSO)算法对模糊控制器量化因子和比例因子进行优化。该方法将ABC算法中的搜索算子作为变异算子引入到QPSO算法中,使得IQPSO算法较好地克服了QPSO算法保持种群多样性差容易早熟收敛的缺陷,并以ITAE指标作为IQPSO算法的适应度函数对模糊控制器进行优化。典型工业过程仿真结果表明,IQPSO优化的模糊控制器具有比PID控制器和标准QPSO优化的模糊控制器更好的控制性能和适用性。     

基于免疫遗传算法优化的模糊控制方法及其应用

在模糊控制器的设计过程中，为了使模糊控制器的性能达到全局优化，应用免疫遗传算法对模糊控制器参数进行优化设计；在综合考虑各种参数对控制器性能影响的基础上，给出了一种全面优化隶属度函数、比例因子和量化因子的优化方法；利用了免疫算法能保持个体的多样性和能对学习过程进行引导的特点，对模糊控制器的多个参数同时进行优化，从而显著提高了系统的收敛性、稳定性。应用该方法对数控铣削加工过程的模糊控制器的设计进行了仿真，并与其他方法进行比较和控制实例的验证，表明了该基于免疫遗传算法优化的模糊器能获得更优良的控制性能。     

高精度实时荧光定量PCR温控系统的设计与仿真

针对目前国产实时荧光定量PCR温控系统升降温速度慢和精度低的问题,提出了一种改进的模糊自适应PID算法。首先根据热力学公式,建立了实时荧光定量PCR温控系统的数学模型,在模糊自适应PID算法的基础上加了一个在线调整量化因子和比例因子的智能调整器,根据设定的模糊控制规则实时优化PID参数、量化因子和比例因子,由PID控制器和智能调整器控制实时荧光定量PCR的温度。采用Simulink对PCR温控系统进行仿真,结果表明：设计的模糊自适应PID控制器控制精度达到[0.1 ℃],快速稳定以后,能达到[0.01 ℃],升降温速度大于等于[7.0 ℃/s],完全能够满足实时荧光定量PCR温控系统对温度精度和升降温速度的要求。     

参数自调整模糊控制器在中央空调控制系统中的应用

针对典型的非线性、时变、滞后系统——中央空调温度控制过程，在分析量化因子和比例因子对系统性能影响的基础上，本文提出了参数自调整模糊控制算法。仿真表明，参数自适应控制器的控制性能优于常规模糊控制器。     

一种高阶模糊CMAC自适应控制及其应用

提出了一种高阶模糊小脑模型神经网络控制器(HOFCMAC),利用模糊子集对输 入状态空间进行分割,同时采用多层的量化方式对输入状态进行量化,并利用代数积,代数和 的方法综合各种量化方式的量化结果.由于多层量化方式的应用,这种控制器也比单纯基于 广义基函数的模糊CMAC有更好的控制性能.复杂工业炉温控制试验结果证明这种方法的 有效性.     

Stability Analysis of Parallel Fuzzy P ＋ Fuzzy I ＋ Fuzzy D Control Systems

The study presented in this paper is in continuation with the paper published by the authors on parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative (FP + FI + FD) controller. It addresses the stability analysis of parallel FP + FI + FD controller. The famous"small gain theorem" is used to study the bounded-input and bounded-output (BIBO) stability of the fuzzy controller. Sufficient BIBO-stability conditions are developed for parallel FP + FI + FD controller. FP + FI + FD controller is derived from the conventional parallel proportional plus integral plus derivative (PID) controller. The parallel FP + FI + FD controller is actually a nonlinear controller with variable gains. It shows much better set-point tracking, disturbance rejection and noise suppression for nonlinear processes as compared to conventional PID controller.     

Design and analysis of region-wise linear fuzzy controllers

In this paper, a methodology to reduce the complexity of a robust controller based on fuzzy if-then rules is proposed. The motivation and the design of this complexity-reduced fuzzy controller are presented. This fuzzy controller with the triangular membership functions and fuzzy partition methods used here leads to a region-wise linear fuzzy controller (RLFC). The properties of the region-wise linear fuzzy controllers are discussed and the reasons why they in general perform better than the PD controllers are also provided. And the simulation results based on a second order plant are included to show that the region-wise linear fuzzy controller outperforms the PD controller. We also show that the region-wise linear fuzzy controller and original fuzzy controller have similar performances.     

平面二级倒立摆基于参数优化的模糊控制

对基于XY平台的平面二级倒立摆进行运动学和动力学分析,得到系统状态方程。通过构造综合误差E和综合误差变化率EC,减少输入变量维数设计模糊控制器,使模糊控制器的控制规则更为简单、有效,同时对量化因子参数进行优化。     

Adaptive fuzzy PIMD controller for systems with uncertain deadzones

In this paper, a new adaptive fuzzy Proportional-Integral (of a modified error function)-Derivative (PIMD) controller is designed for systems with uncertain deadzones. Instead of using the summation of the system output error to be one of the input variables, the fuzzy mechanism in PIMD controller takes the summation of a proposed error function as one essential part of the output fuzzy singleton. Together, with the linearly combined error and difference of the error as the only input variables, the complexity reduced fuzzy mechanism of the fuzzy PIMD controller is constructed. The adaptation processes are provided to determine the parameters of the PIMD controller to reduce the overshoot and to accelerate the system with deadzone to the desired output. The fuzzy PIMD controller is indicated to be flexible to the variations of deadzone parameters. Also, the proposed fuzzy PIMD controller is flexible to the change of deadzone model to contain jump discontinuity points. Moreover, the fuzzy PIMD controller can perform well for the system with time-varying deadzone model. Simulation results are included to indicate the effectiveness of the adaptive fuzzy PIMD controller.     

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.     

Self-organizing adaptive fuzzy neural control for the synchronization of uncertain chaotic systems with random-varying parameters

This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) for the synchronization of uncertain chaotic systems with random-varying parameters. The proposed SAFNC system is composed of a computation controller and a robust controller. The computation controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principle controller. The SOFNN identifier is used to online estimate the compound uncertainties with the structure and parameter learning phases of fuzzy neural network (FNN), simultaneously. The structure-learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. The robust controller is used to attenuate the effects of the approximation error so that the synchronization of chaotic systems is achieved.All the parameter learning algorithms are derived based on the Lyapunov stability theorem to ensure network convergence as well as stable synchronization performance. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.     

Granular computing in the development of fuzzy controllers

This study elaborates on the role of information granularity in the development of fuzzy controllers. As opposed to numeric data being commonly accepted by fuzzy controllers, we discuss a general processing framework involving data-information granules exhibiting various levels of information granularity. The paper analyzes an impact of information granularity on the performance of the controller. We study a way in which information granules arise in control problems, elaborate on a way of describing these granules as well as provide a way of quantifying the level of information granularity. A number of analysis and design issues are studied including robustness of the fuzzy controller, representation of linguistic information and quantification of its granularity. Nonlinear characteristics of the compiled version of the fuzzy controller operating in presence of granular information are discussed in detail. Illustrative numerical examples are provided as well. ©1999 John Wiley & Sons, Inc.     

T-S模糊系统输出反馈控制器的稳定性分析与设计

输出反馈控制是T-S模糊控制系统设计的一种重要方法．本文提出了一类由模糊状态观测器和模糊调节器构成的输出反馈控制器稳定性分析和解析设计的新方法．为了减小稳定性分析的保守性和难度,本文充分利用了模糊规则前件变量模糊隶属度函数的结构信息,对前件变量采用标准模糊分划的T-S模糊系统输出反馈控制器进行了研究,获得了一些新的稳定性条件．然后采用平行分布补偿法(PDC)和线性矩阵不等式方法(LMI),研究了该类输出反馈控制器的解析设计方法．通过一个非线性质量块-弹簧-阻尼器系统输出反馈控制器的设计和计算机仿真,验证了本文方法的有效性．     

Structure and stability analysis of a Takagi–Sugeno fuzzy PI controller with application to tissue hyperthermia therapy

 In this paper, we first reveal the analytical structure of a simple Takagi–Sugeno (TS) fuzzy PI controller relative to the linear PI controller. The fuzzy controller consists of two linear input fuzzy sets, four TS fuzzy rules with linear consequent, Zadeh fuzzy logic AND and the centroid defuzzifier. We prove that the fuzzy controller is actually a nonlinear PI controller with the gains changing with process output. Utilizing the well-known small Gain Theorem in control theory, we then derive sufficient conditions for global stability of the fuzzy control systems involving the TS fuzzy PI controller. Finally, as an application demonstration, we apply the fuzzy PI controller to control issue temperature, in computer simulation, during hyperthermia therapy. The relationship between heat energy and tissue temperature is represented by a linear time-varying model with a time delay. The sufficient conditions for global stability are used to design a stable fuzzy control system. Our simulation results show that the fuzzy PI control system achieves satisfactory temperature control performance. The control system is robust and stable even when the model parameters are changed suddenly and significantly.