一种改进的三维模糊控制设计方法

针对传统三维模糊控制器模糊规则多,在实际应用中不易实现,输入量之间相互耦合的问题,提出一种PID与模糊混合(M)型控制器.该控制策略是把三个输入量分别为偏差、偏差变化、偏差的偏差变化的一维模糊控制器加权融合实现混合(M)型控制器,解决了多变量模糊控制中的规则数量几何爆炸问题,具有更大的可控范围.针对传统模糊控制不能消除系统静差的缺陷,吸收增量式PID控制递推累加的形式,对偏差进行积分,混合(M)型控制器实现了对系统静差的消除.此外介绍了M型控制器的参数整定方法,并对其稳定性进行了分析.仿真表明,M型控制器控制效果比二维模糊控制和PID控制都好.     

基于LQR的二级倒立摆的变论域自适应模糊控制

针对传统模糊控制的不足,以二级倒立摆为例,采用了变论域自适应模糊控制理论.考虑到二级倒立摆为多变量系统,为了解决模糊控制器规则组合爆炸问题,利用LQR理论先设计出状态反馈器,再进行降维处理.最后利用变论域自适应模糊控制理论给出伸缩因子,从而得到变论域自适应模糊控制器.仿真结果表明,该方法控制效果好,鲁棒性强.     

规则自适应模糊PID数字电压调节器设计

应用自适应模糊控制理论,结合航空电源发电机控制器系统的实际情况,设计了基于规则自适应模糊PID算法的数字电压调节器系统.介绍了系统的硬件组成和相应规则自适应模糊PID控制器算法的具体实现.该算法克服了不能建立精确的数学模型所带来的弊端,经过验证,该系统具有良好的稳定性和快速的跟踪性,具有一定的现实意义.     

直流调速系统的模糊／PID控制器设计

本文采用模糊/PID控制策略对直流调速系统的转速环进行了设计,根据转速误差的阀值由模糊控制切换到PID控制,给出了模糊控制规则和PID控制器各参数确定的方法,并利用Matlab进行了仿真验证,结果表明模糊/PID控制器具有较好的控制性能。     

基于MATLAB的石灰窑模糊控制器设计

采用MATLAB的模糊逻辑工具箱,详细介绍了石灰窑模糊控制嚣的设计过程.针对单纯模糊控制存在的问题,在模糊控制器的设计基础上,设计了模糊PID控制器.并对模糊PID控制性能进行了仿真.     

无刷直流电动机模糊自适应PID控制策略的仿真研究

在无刷直流电动机控制系统中,如何提高系统的控制精度、稳定性及鲁棒性备受关注。当无刷直流电动机系统的负载或参数发生变化时,传统的PID控制策略很难达到预期的效果。文章基于模糊控制理论,建立了模糊推理规则表,设计了模糊控制器和模糊自适应PID控制器,并对模糊自适应PID控制策略在无刷直流电动机系统中的应用进行了仿真研究。仿真结果表明,模糊自适应PID控制策略能加快无刷直流电动机系统的响应速度,并具有较强的鲁棒性。     

自组织模糊控制器

本文所述自组织模糊控制器由基本模糊控制器和自组织控制两部分组成.自组织控 制部分在系统运行中根据测得的系统性能,修改基本模糊控制,以获得较好的性能.因而 它又由"性能测量"、"控制量校正"和"修改模糊控制规则"三部分组成.为了在实时控制 中减少计算机存贮容量和降低速度要求,我们提出了直接修改模糊控制表算法.理论和 试验表明,其效果与修改基本模糊控制规则是一致的.     

模糊控制在磁悬浮球系统实时控制中的应用

在磁悬浮球系统中采用PID控制时,动态性能较差,控制效果不理想.为了获得控制效果较好的控制器,根据模糊控制原理,设计磁悬浮模糊控制器.根据PID实时控制中的经验,确定模糊控制器的输入输出变量模糊化范围,建立合理的模糊规则表.在Matlab/Simulink中组建实时控制模块,并分别对磁悬浮球系统在无干扰和有干扰下进行实时控制.对控制效果进行分析讨论,证明模糊控制器具有良好的鲁棒性和在非线性系统控制中的有效性.     

改进型模糊神经网络PID控制器的设计与仿真

传统PID控制器存在控制参数无法在线调整、控制效果差等问题。为了解决这些问题,本文提出了一款基于改进型模糊神经网络的智能PID控制器。该控制器不仅融合了模糊控制的推理能力和神经网络的学习能力,还创造性地将模糊规则参数化,使模糊规则也可以在线调整,进而提高了控制的准确性。同时,通过建构新型激活函数——IThLU函数,有效地避免梯度消失及梯度爆炸现象的发生,提高了控制的响应性。最终的仿真实验结果表明：这种改进型模糊神经网络智能PID控制器可以实现控制参数的在线实时调整,提高系统的响应性、稳定性和准确性,是对PID控制算法的有效改进。     

大滞后系统的专家-模糊PID控制器设计

在工业过程中,大滞后系统的时滞、非线性情况严重,常规PID控制往往难以获得理想的控制效果.针对上述情况,设计了一种基于专家经验的模糊与PID并联复合型控制器,能够在PID控制的基础上改善系统的控制品质.介绍了带有可调因子的模糊控制算法,给出了模糊控制器相关参数的整定方法.根据专家经验制定了模糊控制与PID控制的加权系数的整定规则.对具有大迟延、大滞后特性的循环流化床的燃料量与床温模型进行了仿真研究.仿真结果表明:在大滞后系统的控制中,所设计的专家模糊PID控制器能够很大程度的改善系统的动态动性,且具有较好的鲁棒性.     

Adaptive hierarchical tuning of fuzzy controllers

Fuzzy controller design includes both linear and non-linear dynamic analysis. The knowledge base parameters associated within the fuzzy rule base influence the non-linear control dynamics while the linear parameters associated within the fuzzy output signal influence the overall control dynamics. For distinct identification of tuning levels, an equivalent linear controller output and a normalized non-linear controller output are defined. A linear proportional-integral-derivative (PID) controller analogy is used for determining the linear tuning parameters. Non-linear tuning is derived from the locally defined control properties in the non-linear fuzzy output. The non-linearity in the fuzzy output is then represented in a graphical form for achieving the necessary non-linear tuning. Three different tuning strategies are evaluated. The first strategy uses a genetic algorithm to simultaneously tune both linear and non-linear parameters. In the second strategy the non-linear parameters are initially selected on the basis of some desired non-linear control characteristics and the linear tuning is then performed using a trial and error approach. In the third method the linear tuning is initially performed off-line using an existing linear PID law and an adaptive non-linear tuning is then performed online in a hierarchical fashion. The control performance of each design is compared against its corresponding linear PID system. The controllers based on the first two design methods show superior performance when they are implemented on the estimated process system. However, in the presence of process uncertainties and external disturbances these controllers fail to perform any better than linear controllers. In the hierarchical control architecture, the non-linear fuzzy control method adapts to process uncertainties and disturbances to produce superior performance.     

A systematic study of fuzzy PID controllers-function-basedevaluation approach

A function-based evaluation approach is proposed for a systematic study of fuzzy proportional-integral-derivative (PID)-like controllers. This approach is applied for deriving process-independent design guidelines from addressing two issues: simplicity and nonlinearity. To examine the simplicity of fuzzy PID controllers, we conclude that direct-action controllers exhibit simpler design properties than gain-scheduling controllers. Then, we evaluate the inference structures of direct-action controllers in five criteria: control-action composition, input coupling, gain dependency, gain-role change, and rule/parameter growth. Three types of fuzzy PID controllers, using one-, two- and three-input inference structures, are analyzed. The results, according to the criteria, demonstrate some shortcomings in Mamdani's two-input controllers. For keeping the simplicity feature like a linear PID controller, a one-input fuzzy PID controller with "one-to-three" mapping inference engine is recommended. We discuss three evaluation approaches in a nonlinear approximation study: function-estimation-based, generalization-capability-based and nonlinearity-variation-based approximations. The study focuses on the last approach. A nonlinearity evaluation is then performed for several one-input fuzzy PID controllers based on two measures: nonlinearity variation index and linearity approximation index. Using these quantitative indices, one can make a reasonable selection of fuzzy reasoning mechanisms and membership functions without requiring any process information. From the study we observed that the Zadeh-Mamdani's "max-min-gravity" scheme produces the highest score in terms of nonlinearity variations, which is superior to other schemes, such as Mizumoto's "product-sum-gravity" and "Takagi-Sugeno-Kang" schemes     

Combining fuzzy, PID and regulation control for an autonomous mini-helicopter

This paper reports on the synthesis of different flight controllers for an X-Cell mini-helicopter. They are developed on the basis of the most realistic mathematical model currently available. Two hybrid intelligent control systems, combining computational intelligence methodologies with other control techniques, are investigated. For both systems, Mamdani-type fuzzy controllers determine the set points for altitude/attitude control. These fuzzy controllers are designed using a simple rule base. The first scheme consists of conventional SISO PID controllers for z-position and roll, pitch and yaw angles. In the second scheme, two of the previous PID controllers are used for roll and pitch, and a linear regulator is added to control altitude and yaw angle. These control schemes mimic the action of an expert pilot. The designed controllers are tested via simulations. It is shown that the designed controllers exhibit good performance for hover flight and control positioning at slow speed.     

关于模糊PID控制器推理机维数的研究

对一维(1D)至三维(3D)模糊PID控制器进行了系统的分析研究,提出了四项系 统功能特性指标来评价不同结构的控制器;这包括控制分量合成,耦合影响,增益相关和规则 增长.通过对最常见的二维Mamdani模糊控制器进行分析研究,发现该控制器存在功能缺 陷.为此,提出了最优结构的一维模糊PID控制器.该控制器采用了"1D-3D"映射关系的模糊 推理机,从而实现了三个控制分量可以独立不相关的调整功能.通过与二维和三维控制器比 较结果表明,一维控制器具有最佳系统功能特性.     

Structural analysis of fuzzy controllers with nonlinear input fuzzy sets in relation to nonlinear PID control with variable gains

The popular linear PID controller is mostly effective for linear or nearly linear control problems. Nonlinear PID controllers, however, are needed in order to satisfactorily control (highly) nonlinear plants, time-varying plants, or plants with significant time delay. This paper extends our previous papers in which we show rigorously that some fuzzy controllers are actually nonlinear PI, PD, and PID controllers with variable gains that can outperform their linear counterparts. In the present paper, we study the analytical structure of an important class of two- and three-dimensional fuzzy controllers. We link the entire class, as opposed to one controller at a time, to nonlinear PI, PD, and PID controllers with variable gains by establishing the conditions for the former to structurally become the latter. Unlike the results in the literature, which are exclusively for the fuzzy controllers using linear fuzzy sets for the input variables, this class of fuzzy controllers employs nonlinear input fuzzy sets of arbitrary types. Our structural results are thus more general and contain the existing ones as special cases. Two concrete examples are provided to illustrate the usefulness of the new results.     

基于PLC的过程控制实验装置温度模糊PID控制

本文介绍了用S7-200实现过程控制系统实验装置中锅炉夹套的温度模糊控制设计思想,对模糊PID控制的结构、模糊PID控制器的设计、模糊PID控制的PLC实现进行了分析,文中详细介绍了模糊控制器程序的编写方法,结果表明,用PLC实现的模糊控制器简单实用。     

在线自调整模糊-PID控制器的设计

提出一种带修正因子的在线自调整模糊－ＰＩＤ控制器的设计,即将模糊控制器与ＰＩＤ控制器考虑一个系统中,通过在线调整协调因子改变上述两个控制器输出的权重,同时在模糊控制器环节采用修正因子对其输出在线调整。仿真证明,该方法具有算法简单、控制器质较好等优点。     

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。     

一类模糊P I D 控制器的鲁棒优化设计

研究一类模糊 PID控制器的鲁棒设计。以小增益定理分析得到该模糊 PID控制系统稳定性条件。针对参数摄动系统的“最坏点”,用该稳定性条件作为约束 ,采用遗传算法对标称系统的性能进行优化 ,求得优化鲁棒控制器。以倒立摆为例进行鲁棒模糊 PID控制器的设计 ,实验结果表明了该方法的有效性     

基于模糊推理的自整定PID控制器

设计了一种新的模糊PID控制器。用基于给定的相角裕度和幅值裕度的方法给出了PID控制器参数的初值。在运行过程中 ,用模糊控制器在线调整PID参数 ,使过程具有较高的控制品质