应用进化策略的城域交叉路口分级模糊控制

提出一种面向城域单交叉路口的自适应分级模糊控制系统，采用进化策略对分级模糊控制器的模糊隶属度函数进行离线优化调整．该控制系统不仅具有分级模糊控制的优点，同时能使模糊隶属度函数根据不同的交通情况自适应地变化，从而改善控制效果．对一个具有直行和左转车流运动的四向交叉路口进行的仿真表明了该方法能比定时控制和隶属度函数固定的分级模糊控制取得更好的控制效果．     

混沌系统的一种自学习模糊控制

提出一种基于遗传算法的自学习模糊控制方法控制混沌系统。用一种改进的遗传算法学习模糊控制器的隶属度函数，以改善模糊控制器的性能，使其达到良好的控制效果。用此方法控制Ｈｅｎｏｎ系统的混沌行为，效果良好。     

汽车智能防撞自适应控制研究与仿真

针对汽车防撞模糊控制模型不能自动调整参数的缺点,建立汽车防撞自适应模糊推理模型。采用混合学习算法对自适应模糊推理模型的前提参数和结论参数进行辨识,以加速收敛。经模拟训练和仿真输出结果证明,该模型能够对汽车防撞模糊控制器隶属函数和模糊规则进行优化,较好地实现紧急报警情况下的汽车防撞自适应控制。     

A hybrid frequency—time domain adaptive fuzzy control scheme for flexible link manipulators

This paper addresses the implementation of an adaptive fuzzy controller for flexible link robot arms. The design technique is a hybrid scheme involving both frequency and time domain techniques. The eigenvalues of the open loop plant can be estimated through application of a frequency domain based identification algorithm. The region of the eigenvalue space, within which the system operates, is partitioned into fuzzy cells. Membership function are assigned to the fuzzy sets of the eigenvalue universe of discourse. The degree of uncertainty on the estimated eigenvalues is encountered through these membership functions. The knowledge data base consists of feedback gains required to place the closed loop poles at predefined locations. A rule based controller infers the control input variable weighting each with the value of the membership functions at the identified eigenvalue. The afore-mentioned controller is compared through simulation with conventional techniques, namely pole placement and gain scheduling.     

无刷直流电机模糊PI系统的建模及仿真

针对无刷直流电机的传统PI控制策略存在的局限性,即PI控制器控制参数不能随环境变化而调整,不具有整体优化功能,给出了一种模糊PI控制建模方法。利用模糊集合的隶属度函数,模糊控制规则以及清晰化方法,基于Matlab/Simulink平台并结合电机转子位置信号,建立无刷直流电机模糊PI控制系统的仿真模型。仿真结果表明,模糊PI控制加快了系统响应时间、减小了超调,具有较强的鲁棒性和自适应能力,使系统获得了较好的控制性能,具有较好的应用价值。     

FUZZY SLIDING MODE CONTROLLER DESIGN: INDIRECT ADAPTIVE APPROACH

In this paper, using the concept of sliding mode control SMC, a fuzzy sliding mode controller FSMC, which is synthesized by linguistic control rules, is proposed. Two sets of fuzzy rule bases are utilized to represent the controlled system. The membership functions of the THEN-part, which is used to construct a suitable equivalent control of SMC, are changed according to adaptive law. In particular, only one adaptive factor is characterized to adapt the membership functions instead of several ones in conventional adaptive approaches. Under this design scheme, we not only maintain the distribution of membership functions over state space but also reduce considerably computing time. The proposed indirect adaptive FSMC is synthesized through the following stages. First, we construct the fuzzy rule bases according to the common sense of SMC to describe the model of the controlled system, and define the fuzzy sets whose membership functions are equally distributed in state space. Then, the derived adaptive law is used to adjust the membership functions of the THEN-part to approximate an equivalent control without knowing the mathematical model of the controlled system. Third, a hitting control is developed to guarantee the stability of the control system. Finally, we smooth the hitting control via proposed heuristic control rules. We apply this FSMC to controlling a nonlinear inverted pendulum system to confirm the validity of the proposed approach.     

采用遗传优化自适应模糊PID控制球杆系统

为解决球杆系统动态、静态性能不高的问题,提出了遗传算法优化自适应模糊PID控制器的控制方法.该模型在拉格朗日方程建立球杆系统数学模型的基础上,采用遗传算法优化模糊控制规则、隶属函数和自适应PID参数.在GBB1004系统中建立了遗传算法优化后的自适应模糊PID控制器以及控制模型,并对该控制器进行实验验证.实验结果证明了遗传算法优化后的模糊控制器有效地减小了系统的超调量,缩短了系统的调节时间,能够较好地控制球杆系统.     

自适应事件触发网络化非线性系统滤波器设计

本文针对网络化非线性控制系统设计了基于自适应事件触发方案满足H∞性能的模糊滤波器.第一,提出新的自适应事件触发方案来决定数据包是否需要传输到通信网络中,从而提高网络资源利用率;第二,运用模糊线积分的方法,构建模糊李雅普诺夫泛函,避免求解隶属函数的时间微分;第三,利用Wirtinger不等式及倒凸引理结合的技术,得到滤波...     

一种变论域模糊控制自适应算法

针对受控系统非线性、时变性、复杂性和不确定性的特点, 文中采用一种基于模糊控制的变论域自适应控制算法, 将变论域引入模糊控制的隶属函数中, 设计了输入函数的自适应律, 并通过李雅普诺夫函数进行了稳定性分析. 仿真结果表明在有干扰的情况下, 算法能很好的跟踪系统输入, 使系统跟踪误差小且能够保证系统的稳定性.该算法结构简单, 具有良好的鲁棒性和动态性能, 同时克服了系统参数变化对稳定性造成的不利影响. 仿真实例表明了本算法的正确性和有效性.     

Adaptive fuzzy controller design with observer for a class of uncertain nonlinear MIMO systems

In this paper, an adaptive fuzzy control approach is proposed to stabilize a class of uncertain nonlinear MIMO systems with the unmeasured states and the external disturbances. The fuzzy logic systems are used to approximate the unknown functions. Because it does not required to assume that the system states are measurable, it needs to design an observer to estimate the system unmeasured states. The considered MIMO systems are more general, i.e. they consist of N subsystems and each subsystem is in the non‐affine form. The stability of the closed‐loop system is verified by using Lyapunov analysis method. Two simulation examples are utilized to verify the effectiveness of the proposed approach. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

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.     

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 NEURO‐FUZZY SYSTEM DESIGN METHODOLOGY FOR VIBRATION CONTROL

Fuzzy system has been known to provide a framework for handling uncertainties and imprecision by taking linguistic information from human experts. However, difficulties arise in determining effectively the fuzzy system configuration, i.e., the number of rules, input and output membership functions. A neuro‐fuzzy system design methodology by combining neural network and fuzzy logic is developed in this paper to adaptively adjust the fuzzy membership functions and dynamically optimize the linguistic‐fuzzy rules. The structure of a five‐layer feedforward network is shown to determine systematically the correct fuzzy logic rules, tune optimally (in the sense of local region) the parameters of the membership functions, and perform accurately the fuzzy inference. It is shown both numerically and experimentally that engineering applications of the neuro‐fuzzy system to vibration control have been very successful.     

基于模糊神经网络的模型参考自适应控制

用模糊神经网络作为控制器,依靠参考模型产生理想的控制系统闭环响应,从而随时得 到控制系统的输出误差.用梯度法实时修正模糊控制器的输入和输出隶属度参数,得到一种 在线模糊自适应控制的新方法.通过倒立摆的仿真实验表明,该方法是可行的并能适应对象 特性的大范围变化.     

基于模糊PID的整平机姿态控制算法设计与实现

针对目前国内的整平机整平精度低、抗干扰能力弱、动态响应时间长等问题,对整平机姿态控制算法进行了研究,比较并分析了PID控制和模糊控制的优缺点,设计了基于模糊PID的整平机姿态控制算法。在Matlab/Simulink仿真环境下,选定了合适的隶属度函数、模糊推理法和解模糊法,调试得出了使系统达到最优控制效果的模糊规则库。以STM32F407为处理器实现了该算法,在整平机实验平台上进行了姿态控制实验。结果表明模糊PID控制算法能够使整平机平地铲在扰动下保持良好的控制效果,控制精度和响应速度较常规PID有了大幅提升,在农业和建筑业平地系统中具有很强的应用价值。     

基于多分辨率分析的T-S模糊系统

目前模糊系统缺乏保持辨识精度与模糊语义最佳折中的有效辨识方法,其主要原因在于缺乏系统的优化结构辨识方法.因此,本文从时-频域角度构造出基于多分辨率分析的T-S(Takagi-Sugeno)模糊系统拓扑结构.然后,采用具有多分辨率特点的B-样条尺度函构造模糊隶属函数,根据投影算法和模糊隶属函数相异测度给出了模糊系统结构辨识算法.仿真结果验证了这种模糊系统及其结构辨识算法的有效性.     

三级倒立摆的加权变量模糊神经网络控制

为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani型摸糊推理规则控制器控制倒立摆系统稳定的基础上,设计了一种更有效率的基于Sugeno型模糊推理规则的模糊神经网络控制器。该控制器使用BP神经网络和最小二乘法的混合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani型控制器的仿真对比,表明该Sugeno型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。     

自适应神经模糊推理结合PID控制的并联机器人控制方法

针对6自由度液压驱动并联机器人的精确控制问题,提出一种结合自适应神经模糊推理系统(ANFIS)和比例积分微分(PID)控制的机器人控制方法。首先,利用浮动坐标系描述法(FFRF)来模拟机器人柔性组件,并构建并联机器人的拉格朗日动力学模型。然后,根据模糊推理中的模糊规则来自适应调整PID控制器参数。最后,利用神经自适应学习算法使模糊逻辑能计算隶属度函数参数,从而使模糊推理系统能追踪给定的输入和输出数据。将该控制器与传统PID控制器、模糊PID控制器进行比较,结果表明,ANFIS自整定PID控制器大大减小了末端器位移误差,能很好的控制并联机器人末端机械手的运动。     

单点交通信号控制系统的优化设计与仿真

针对城市固定相序的单点交叉口多相位交通信号进行控制,设计了基于车流量预测的动态调整相位最大绿灯时间的模糊控制系统。综合评估当前相位、后续相位的交通需求度,以此决定绿灯时间分配。系统采用遗传算法对模糊隶属度函数进行优化调整,使隶属函数的选取更为合理,随交通状况的改变自适应地调整。仿真结果表明,该方法能有效降低通行车辆在交叉口的平均等待时间,提高平均车流速度,控制效果明显优于传统控制方法。     

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.