Analytical Analysis on the Structure of a Typical Fuzzy On - Off Controller

The analytical structure of a typical fuzzy on - off controller that employs three or more triangular Input fuzzy sets, Zadeh fuzzy logic AND operator, fuzzy rules with singleton output fuzzy sets, and the centriod defuzzifier is Investigated in this paper. The analytical expressions of the variable gains of the fuzzy controller are derived. The resulting explicit structure shows that the fuzzy controller is accurately a nonlinear PD - like controller with gains continuously changing with system output in different regions of input space.

ACS algorithm-based adaptive fuzzy PID controller and its application to CIP-I intelligent leg

Based on the ant colony system （ACS） algorithm and fuzzy logic control, a new design method for optimal fuzzy PID controller was proposed. In this method, the ACS algorithm was used to optimize the input/output scaling factors of fuzzy PID controller to generate the optimal fuzzy control rules and optimal real-time control action on a given controlled object. The designed controller, called the Fuzzy-ACS PID controller, was used to control the CIP-Ⅰ intelligent leg. The simulation experiments demonstrate that this controller has good control performance. Compared with other three optimal PID controllers designed respectively by using the differential evolution algorithm, the real-coded genetic algorithm, and the simulated annealing, it was verified that the Fuzzy-ACS PID controller has better control performance. Furthermore, the simulation results also verify that the proposed ACS algorithm has quick convergence speed, small solution variation, good dynamic convergence behavior, and high computation efficiency in searching for the optimal input/output scaling factors.

ADAPTIVE FUZZY CONTROL FOR ROBOT ARM MANIPULATOR WITH 5-DOF

To control the robot and track the designed trajectory with uncertain disturbances in a specified precision range, an adaptive fuzzy control scheme for the robot arm manipulator is discussed. The controller output error method （COEM） is used to design the adaptive fuzzy controller. A few or all of the parameters of the controller are adjusted by using the gradient descent algorithm to minimize the output error. COEM is adopted in the adaptive control system for the robot arm manipulator with 5-DOF. Simulation results show the effectiveness of the method and the real time adjustment of the parameters.

Adaptive Sliding Mode Control for MIMO Nonlinear Systems Based on Fuzzy Logic Scheme

In this study an indirect adaptive sliding mode control (SMC) based on a fuzzy logic scheme is proposed to strengthen the tracking control performance of a general class of multi-input multi-output (MIMO) nonlinear uncertain systems. Combining reaching law approach and fuzzy universal approximation theorem, the proposed design procedure combines the advantages of fuzzy logic control, adaptive control and sliding mode control. The stability of the control systems is proved in the sense of the Lyapunov second stability theorem. Two simulation studies are presented to demonstrate the effectiveness of our new hybrid control algorithm.

Fuzzy adaptive learning control network with sigmoid membership function

To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.

基于混沌控制的采摘机器人防碰撞系统设计

针对采摘机器人在作业过程中遇到障碍物导致路径规划出现混沌现象的问题,基于混沌控制对采摘机器人防碰撞系统进行设计,并采用模糊自适应控制算法和滑模变结构控制算法结构的方式对系统进行控制。为了验证防碰撞系统的有效性,对采摘机器人进行了防碰撞和采摘仿真试验,结果表明:采摘机器人在作业过程中可以避开障碍物,按照规划路线到达预设位置,完成果蔬的采摘。

An active vibration control method based on energy-fuzzy for cantilever structures excited by aerodynamic loads

In wind tunnel tests for the full-model fixed with sting,the low structural damping of the long cantilever sting results in destructive low-frequency and large-amplitude vibration.In order to obtain high-quality wind tunnel test data and ensure the safety of wind tunnel tests,an energy-fuzzy adaptive PD(Proportion Differentiation)control method is proposed.This method is used for active vibration control of a cantilever structure under variable aerodynamic load excitation,and real-time adjustment of parameters is achieved according to the system characteristics of vibration energy.Meanwhile,a real-time method is proposed to estimate the real-time vibration energy through the vibration acceleration signal,and the average exciting power of aerodynamic load is obtained by deducting the part of the power contributed by the vibration suppressor from the total power.Furthermore,an energy-fuzzy adaptive PD controller is proposed to achieve adaptive control to the changes of the aerodynamic load.Besides,the subsonic and transonic experiments were carried out in wind tunnel,the results revealed that comparing to fixed gain PD controllers,the energy-fuzzy adaptive PD controller maintains higher performance.

基于FUZZY-PID的主汽温控制

基于ＳＴＡＮ－９０火电机组全仿真机系统平台，选用全论域范围内带自调整因子的模糊控制器与ＰＩ控制器相结合的复合控制方案设计过热器减温控制系统：编程建立相关模糊控制算法并组态实现了控制系统功能．动态试验结果表明，与常现ＰＩＤ串级控制系统相比该系统达到了更好的动、静态性能。

基于遗传算法的FUZZY+CMAC优化设计

针对模糊控制器中的参数与CMAC权值的调整及学习很困难的问题,采用改进的遗传算法对模糊控制器和CMAC神经网络的比例因子和连接权值进行寻优,把模糊控制、CMAC和遗传算法的优点结合起来,设计了基于改进遗传算法的FUZZY+CMAC控制系统,给出了系统的整体结构和各部分的控制设计。仿真结果表明,该控制系统具有超调量小和实时性强的特点。

基于误差驱动的混合型模糊PD控制器设计

提出一种基于误差驱动的混合型模糊PD控制器。对其结构进行分析,并对模糊控制理论未解决的规则选取问题进行探讨。模糊控制器采用“1D-1D”的映射关系,结构简单,灵活性强;采用非线性输入输出映射关系控制曲线,仿真结果证明其性能优于传统线性规则。用波波夫稳定判据得到了控制器稳定的充分条件。

基于模糊变结构补偿的机械臂运动路径避障规划算法

机械臂的活动范围可以自由调整,其准确的运动规律较难获取,导致机械臂运动路径避障规划存在规划效果差、规划路径长以及规划耗时长的问题,为此提出基于模糊变结构补偿的机械臂运动路径避障规划算法。该算法首先对机械臂进行相关动力学分析,将分析结果与模糊变结构相结合对机械臂力矩进行补偿控制;然后使用Q-learning算法计算补偿控制结果,获取路径最佳规划策略;最后通过该策略完成机械臂避障路径的规划。实验结果表明,使用该算法进行路径规划时的规划结果较为准确,规划路径短且规划耗时短。

变论域模糊补偿的机械臂积分终端滑模控制

为了减小机械臂在环境扰动、参数漂移和建模误差等影响下的轨迹跟踪误差,设计了基于积分终端滑模和变论域模糊补偿的组合控制器。采用拉格朗日方程建立了机械臂系统的动力学模型,制定了不确定因素影响下机械臂跟踪控制方案;设计的积分终端滑模控制器可以将系统初始状态限制在滑模面上,消除了控制过程的抖振并提高了跟踪速度;提出了自适应论域策略,该策略可以提高补偿力矩的输出细粒度,并将变论域模糊算法用于不确定因素补偿。经实验验证,变论域模糊补偿控制对关节1角位置的最大跟踪误差为0.103 rad,误差绝对均值为0.025 rad,对关节2角位置的最大跟踪误差为0.073 rad,误差绝对均值为0.012 rad,跟踪控制精度高于模糊补偿控制、RBF-BP控制和自适应鲁棒控制,验证了变论域模糊补偿控制方法的有效性和先进性。

具有输出约束的多机械臂系统模糊控制器设计

针对多机械臂协作搬运同一个物体时的期望轨迹跟踪问题,设计一种带有非零时变参数的模糊自适应控制器。通过一个具有非零参数的模糊逻辑系统(Fuzzy Logic System,FLS)万能逼近原理补偿多机械臂在协调操作中的不确定性,为了提高逼近精度,利用积分障碍李雅普诺夫函数(Integral Barrier Lyapunov Function,IBLF)的稳定性条件,设计可自动更新的逼近误差。当系统状态超出FLS的逼近域时,通过滑模面建立的自适应律使系统状态重新进入FLS定义域内。仿真结果表明,在有限空间中多个机械臂协作将一个物体沿预期轨迹完成控制任务时,既可以避免违反输出限制,又可以使闭环系统全局稳定。

采用Coupland-John降型的区间二型模糊控制器的解析结构推导

区间二型模糊控制器的解析结构在系统稳定性分析中具有重要的作用。针对区间二型模糊控制器的解析结构推导过程繁琐、计算效率较低等问题,设计一种采用乘积型算子和Coupland-John降型算法的区间二型模糊控制器,并代入参数推导出其解析结构。新的区间二型模糊控制器的解析结构推导简便,不需迭代和分区,减少了计算量,易于进行性能分析和参数调节。

汽车电动助力转向系统的匹配分析及优化设计

在对汽车电动助力转向系统(EPS)的结构及其动力学特性分析的基础上,建立了线性三自由度汽车模型与EPS系统的集成数学模型。采用单参数自适应模糊PD控制策略,通过时域和频域的仿真计算,研究了EPS与整车动态性能的匹配关系及EPS主要参数的设计原则,并给出了EPS系统主要参数所必须满足的稳定性准则。在此基础上,建立了汽车系统和电动助力转向系统的综合系统多目标优化设计模型,并采用遗传算法对EPS主要参数进行优化设计。最后,通过试验验证了所设计的硬件参数和控制算法的有效性。

模糊控制器的结构化分析及系统化设计方法

对于模糊控制器的输入变量 ,采用一种新型的不均匀、全交迭、三角形的隶属度函数 ,推导了两输入 ( e,Δe)一输出 ( Δu)的典型模糊控制器输出的解析表达式 ;并对最常用的输入变量各取 5个模糊变量的情况进行分析。在此基础上提出一种模糊控制系统的系统化设计方法 ,可根据已有的 PI/ PD控制器参数设计相应的模糊控制器参数。仿真实验说明了该方法的有效性。

电磁引导智能汽车设计与关键技术研究

基于电磁引导的局部运输系统在工业自动化领域已经广泛应用,然而将这种智能引导技术应用到汽车参与的真实道路交通领域还缺乏充分的安全性保证,尚需要深入的系统性研究来提高在现实交通路况下的适应能力。基于经典的电磁场原理、结合经典控制理论与方法,研究了基于电磁导引的智能汽车在智能交通领域应用的关键问题,并在模型汽车上实现了一种可行的自动导引控制系统和节体转向机制。控制软件应用离散型半模糊控制的PID控制算法以及分段等级棒棒PID控制算法实现了整个系统的最优控制,样机在实验室模拟道路上进行了验证,行驶表现与现实路况较接近,整个控制过程平稳顺畅。

基于BP算法的自适应模糊控制系统研究

在用模糊算法产生模糊规则的过程中，专家信息对算法是否收敛起到了很重要的作用．本文基于模糊控制器的一种解析结构，提出了用ＢＰ算法完善经验规则，加速非线性系统模糊控制过程的方法．

基于模糊自学习的交流直线伺服系统滑模变结构控制

针对直接驱动直线式交流伺服系统，提出了一种基于模糊自学习的滑模变结构控制方案。在常规的滑模控制中引入模糊自学习方法，在不影响滑模控制系统完全鲁棒性的情况下，有效地削弱滑模切换控制所产生的抖振。仿真结果表明该方案对系统参数变化和负载扰动具有很强的鲁棒性，抖振得到了明显的抑制。系统具有良好的动、静态性能。

智能控制算法及其在结构振动控制中的应用

本文首先概述了智能控制理论的形成、发展及主要内容,其中着重介绍了智能控制领域中的三个研究热点:模糊逻辑、神经网络、遗传算法的研究现状和发展方向;其次,详细地介绍了近年来智能控制算法在结构控制上的进展和应用,指出智能控制算法的研究是土木工程结构振动智能控制的重要组成部分,并对其研究和应用发展趋势进行了展望。