基于BP神经网络的Smith-Fuzzy-PID算法在阀门定位中的应用研究

为解决气动调节阀控制过程中出现的超调大、精度低等问题,本文采用BP神经网络整定出较优的PID(Proportional Integral Derivative)控制参数,对Smith预估控制器以及模糊控制器进行设计,实现了基于BP神经网络的Smith-Fuzzy-PID控制方法。搭建了实验平台,通过阶跃响应实验来对控制方法进行验证,验证结果表明,提出的方法调节过程无超调,调节时间仅为1.9 s,定位精度在±0.5%以内,有效提高了系统的稳定性,实现了气动调节阀的快速精准定位。

基于Fuzzy-PID的棉花打顶升降装置设计与试验

为解决棉花打顶机作业过程中由于仿形时与刀盘移动滞后、定位精准度低,导致的棉花漏打、过打及伤苗的问题,基于模糊控制技术对棉花打顶装置高度控制系统进行研究。以3MDZF-6棉花打顶机升降系统为分析对象,确定了刀盘在升降过程中的定位精度要求。以液压阀-液压缸为对象,在AMESim中建立打顶升降装置的控制模型,并将其导入到MatLab中,设计相应的模糊规则和模糊控制器,对其进行仿真分析,从超调量和响应时间两方面验证了PID和模糊PID控制方法的可行性。仿真结果表明:无外界干扰的情况下,PID参数分别为100800、500、30时,输入信号达到稳态的响应时间为0.55s;模糊PID控制下输入信号达到稳态的响应时间为0.31s。经过对比分析固定参数PID控制和模糊PID控制在不同扰动下的控制效果,发现模糊PID控制系统的超调量为0.84%,输入信号达到稳态所需的时间为0.39s,小于固定参数PID的0.64s。该方法实现了打顶升降装置的位移控制,提高了刀盘的定位精度及控制系统的抗干扰性和鲁棒性,可为棉田环境下的棉花打顶升降装置的精确控制问题提供借鉴。

矿井带式输送机液压拉紧Fuzzy-PID控制技术研究

针对矿井传统带式输送机拉紧系统响应速度慢、调节能力差、拉紧控制时变性和非线性等问题,提出了一种矿井带式输送机液压拉紧系统Fuzzy-PID控制(基于模糊算法的PID控制)方法。首先,根据液压拉紧装置建立数学模型,其次通过Matlab内置的Simulink仿真库分别对Fuzzy-PID控制器和PID控制器的液压拉紧系统进行仿真,得出输送带拉紧张力启动响应阶段和张力突变的调节响应图,并做出对比分析。最后,通过试验测试来验证算法模型的有效性。仿真结果表明:矿井带式输送机液压拉紧Fuzzy-PID控制系统不仅在启动阶段还有张力突变过程中都具有更好的稳态性能、更快的响应速度。在拉紧装置启动响应阶段的张力超调量降低了13.5%、到达期望值的时间缩短了0.5 s。在拉紧装置张力突变即模拟拉紧和松带阶段,当张力增加时,Fuzzy-PID控制器的调节速度缩短了0.4 s,超调量下降了4%。当张力减少时,Fuzzy-PID控制器的调节速度缩短了0.3 s,超调量降低了2%。试验结果表明:采用Fuzzy-PID控制的效果更佳优异稳定,且损耗更小。对比于PID控制,Fuzzy-PID控制效果更为良好,平均时间缩短31%且总体趋于稳定。对于矿井带式输送机这种连续运输作业的设备,Fuzzy-PID控制技术为矿井带式输送带平稳运行提供了一定保障,不仅减少了电能浪费,也降低了维护保养带式输送机的保养成本。

Temperature Control Design with Differential Evolution Based Improved Adaptive-Fuzzy-PID Techniques

This paper presents the design and performance analysis of Differential Evolution(DE)algorithm based Proportional-Integral-Derivative(PID)controller for temperature control of Continuous Stirred Tank Reactor(CSTR)plant in che-mical industries.The proposed work deals about the design of Differential Evolu-tion(DE)algorithm in order to improve the performance of CSTR.In this,the process is controlled by controlling the temperature of the liquid through manip-ulation of the coolantflow rate with the help of modified Model Reference Adap-tive Controller(MRAC).The transient response of temperature process is improved by using PID Controller,Differential Evolution Algorithm based PID and fuzzy based DE controller.Finally,the temperature response is compared with experimental results of CSTR.

干涉式闭环光纤陀螺仪的PSO-PID控制优化方法

控制系统的设计会对响应速度快且应用范围较广的数字干涉式闭环光纤陀螺(ICFOG)动态性能产生影响。通过分析ICFOG的工作原理,推导出闭环离散控制系统,并利用粒子群优化算法(Particle Swarm Optimization,PSO)对传统的PID控制器参数进行优化。基于这个优化过程,设计一种新型的PSO-PID复合控制器,以取代传统的PID控制器。通过与其他BP神经网络、模糊控制等方法进行对比凸显该控制方法的优越。通过数字仿真分析显示,跟踪速度相较于BP-PID控制方法提高了1.91倍,相对于PID控制方法提高了3.5倍,相对于F-PID控制方法提高了1.75倍。同时,控制精度相对于BP-PID控制方法提高了46.03%,相对于PID控制方法提高了66.30%,相对于F-PID控制方法提高了45.27%。结果显示,采用PSO-PID控制器能够快速达到控制目标且具有较小的超调量。

基于ABC-PID算法的变量喷雾控制系统设计与试验

针对常用流量调节式变量喷雾系统控制参数调节复杂,导致流量控制响应延迟、超调量大等问题,提出利用寻优性能好的人工蜂群算法整定PID参数,从而达到精准调节系统流量的目标,进而提高变量喷雾系统的工作性能。为验证该系统的控制效果,运用MatLab进行仿真分析,发现ABC-PID控制在超调量、上升时间两方面均优于常规PID和模糊PID。在黑龙江八一农垦大学植保实验室进行室内试验,验证ABC-PID、模糊PID及常规PID的控制精度,结果表明:恒速试验平均施药量误差分别为1.3%、3.1%、4.5%,变速试验平均施药量误差分别为2.1%、3.8%、4.9%。根据上述仿真试验和室内试验结果分析,验证了ABC-PID在控制效果方面优于其他两种控制方法。结合仿真试验、室内试验分析可知:提出引入ABC算法优化常规PID控制参数,在施药量控制精度和减小超调量等方面均表现良好,可为精准变量施药技术提供新的思路。

基于ISSA-Fuzzy-PID算法的无刷直流电机调速控制系统

为了使无刷直流电机(BLDCM)从复杂多变的非线性关系中找出最佳PID参数,提出了一种基于改进麻雀算法(ISSA)优化模糊(fuzzy)控制的调速系统。根据BLDCM工作原理及控制方法在MATLAB平台上搭建仿真模型并加入反向学习策略初始化麻雀种群,形成非线性时变控制系统,并将该优化系统与传统PID以及模糊PID(fuzzy-PID)控制系统进行对比分析。仿真结果表明,基于ISSA-Fuzzy-PID的调速控制系统有较快的动态响应,有效增强了BLDCM转速控制的精度和鲁棒性。

基于Fuzzy-PID的负载敏感挖掘机位姿控制策略

挖掘机执行机构轨迹的精确控制是实现其智能化、无人化发展的基础。针对泵控/阀控相耦合的负载敏感(Load Sensitive,LS)系统挖掘机,提出了一种自适应的模糊PID控制方法(Fuzzy-PID)以实现LS挖掘机执行机构位姿的精确控制。该方法不依赖离线计算,可实现作业过程中PID参数的整定。建立LS挖掘机联合仿真模型对Fuzzy-PID的控制性能进行验证,结果表明,Fuzzy-PID控制精度更高,与PID相比,其均方根误差(Root Mean Square Error,RMSE)减少了23.85%。进一步,通过发动机转速及斗杆运行速度验证了Fuzzy-PID稳定性和响应性。研究结果可为负载敏感系统液压挖掘机智能化升级提供理论指导及工程应用价值。

基于AWF-DPID控制的同步Buck变换器研究

针对当前各种电子设备不断发展对电源管理芯片的精度、速度等要求越来越高的趋势,提出一种基于模糊逻辑的反计算法抗饱和双环PID(AWF-DPID)控制策略,实现同步Buck变换器的恒压限流功能。在双环PID(DPID)的基础上,设计一个二维模糊控制器,实时调整电压环输出参数,在电压环输出设计一个反算法抗积分饱和模块,从而调节Buck变换器的脉冲宽度,更高效精准地实现控制。变换器在10 kHz的频率下工作,给出变换器参数通过软件对不同控制策略进行仿真。结果表明,在实现恒压限流的相同控制目标下,AWF-DPID控制较其他控制响应更迅速、超调量更小稳态误差更小并且纹波系数更小;变输出指令下电压跟随性最好,有良好的动态特性。

基于PSO-BP模糊PID的变距取苗机构控制系统设计

为满足番茄、辣椒等蔬菜作物的移栽需求,基于向下取苗原理设计了一种适用72穴和128穴两种主要番茄钵苗穴盘规格的变距取苗机构,通过建立数学模型获得了取苗机械手参数的目标函数,并利用粒子群和模拟退火混合算法对其结构参数进行优化。同时,为实现变距取苗机构的精确控制,提出了一种基于PSO-BP的模糊PID算法以提高控制精度,介绍了系统的结构与工作原理,并通过选型计算与分析建模建立了控制系统的数学模型。针对传统PID控制器稳定性差、响应速度慢等不足之处,利用PSO-BP模糊PID对控制器的参数进行在线调整,以满足控制过程中对参数的不同需求。仿真结果与试验数据的分析表明:在参数相同条件下,基于PSO-BP模糊PID控制系统系统稳定性更好、响应速度更快,具有良好的鲁棒性,提升取苗成功率的同时降低了基质损伤率,能够满足变距取苗机构高精度快速稳定控制的需求。

基于遗传算法-模糊PID的双喷头FDM型3D打印机温度控制方法

熔融沉积成形(fused deposition modeling,FDM)3D打印需要将打印喷头加热至材料所需温度后才能开始打印。由于单喷头FDM型3D打印机的打印效率较低,以及其加热系统的滞后性较大且稳定性差,使得整个成形过程既耗时又浪费资源,且成形件的质量不高。为解决上述问题,结合打印材料物理性质和化学性质的差异性,提出了一种基于遗传算法-模糊PID(proportional-integral-derivative,比例-积分-微分)的温度控制方法,以实现对双喷头FDM型3D打印机加热方法的控制,并建立温度控制系统的MATLAB/Simulink仿真模型,以验证所提出的控制方法的可靠性。仿真和实验结果表明,与传统PID控制、模糊PID控制相比,遗传算法-模糊PID控制的响应时间缩短了36.03%和32.45%,调节时间缩短了28.06%和20.99%,具有响应速度快、调节时间短、超调量小和控制效果稳定等优势。研究结果可为复合材料的双喷头FDM 3D打印提供参考。

基于SSA-模糊PID的AUV姿态控制研究

针对AUV高精度、高稳定性姿态控制的提升需求,提出一种结合麻雀算法(SSA)和模糊PID控制的姿态控制器;采用麻雀算法对模糊PID控制器参数进行优化,并将寻优后模型用于算法的反馈补偿,有效解决了模糊PID控制过度依赖经验,难以应对水下复杂工况的问题;仿真结果表明,SSA-模糊PID控制器在AUV姿态调节中表现出较传统模糊PID控制器更好的响应速度和抗干扰能力,有效改善了AUV姿态控制性能;经实际应用验证,控制器在复杂工况下可快速收敛至期望姿态并维持稳定。

基于BP-NSGA-Ⅱ优化的高速电梯轿厢水平振动变论域模糊PID控制

针对影响高速电梯乘坐舒适性和安全性的轿厢水平振动问题,提出一种基于反向传播(Backpropagation,BP)神经网络和非支配排序遗传算法-Ⅱ(Non-dominant Sorting Genetic Algorithm-Ⅱ,NSGA-Ⅱ)的变论域模糊PID控制方法。首先建立基于达朗贝尔原理的轿厢动力学模型,其次在传统变论域模糊PID控制的基础上建立以量化因子作为输入,轿厢水平振动加速度均方根和位移均方根作为输出的BP神经网络模型,最后将该模型作为NSGA-Ⅱ算法的适应度函数,通过NSGA-Ⅱ算法优化量化因子来提高系统控制精度。仿真分析结果表明:基于BP神经网络和NSGA-Ⅱ算法的变论域模糊PID控制方法对轿厢水平振动的抑制效果优于变论域模糊PID控制方法。

Fuzzy Feedback Control for Electro-Hydraulic Actuators

Electro-hydraulic actuators(EHA)have recently played a significant role in modern industrial applications,especially in systems requiring extremely high precision.This can be explained by EHA’s ability to precisely control the position and force through advanced sensors and innovative control algorithms.One of the promising approaches to improve control accuracy for EHA systems is applying classical to modern control algorithms,in which the proportional–inte-gral–derivative(PID)algorithm,fuzzy logic controller,and a hybrid of these methods are popular options.In this paper,we developed a novel version of the fuzzy control algorithm and linear feedback control method,namely fuzzy lin-ear feedback control,to improve the control performance.To achieve the highest performance,wefirst designed a mathematical EHA model based on the Matlab/Simulink software packages thanks to the selected parameters,which are similar to a real EHA system.Then,we respectively applied PID,fuzzy PID(FPID),and fuzzy linear feedback control(FLFC)before comparing them to have a full view of the outstanding advantages of the proposed algorithm.The simulation results showed that the proposed FLFC algorithm is approximately 99%and 77%super-ior in performance to the PID and feedback control algorithms,respectively.

An Adaptive Neuro-Fuzzy Inference System to Improve Fractional Order Controller Performance

The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme.In the pro-posed adaptive control structure,the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors(λandµ)of the FOPID(also known as PIλDµ)controller to achieve better control performance.When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters,the stability and robustness of the system can be achieved effec-tively with the proposed control scheme.Also,a modified structure of the FOPID controller has been used in the present system to enhance the dynamic perfor-mance of the controller.An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme.The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters.The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PIλDµand conventional PID control schemes to validate the advantages of the control-lers.The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model.Also,the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time,settling time and error criteria.

Proportion Integration Differentiation(PID)Control Strategy of Belt Sander Based on Fuzzy Algorithm

Aiming at solving the problems of response lag and lack of precision and stability in constant grinding force control of industrial robot belts,a constant force control strategy combining fuzzy control and proportion integration differentiation(PID)was proposed by analyzing the signal transmission process and the dynamic characteristics of the grinding mechanism.The simulation results showed that compared with the classical PID control strategy,the system adjustment time was shortened by 98.7%,the overshoot was reduced by 5.1%,and the control error was 0.2%-0.5%when the system was stabilized.The optimized fuzzy control system had fast adjustment speeds,precise force control and stability.The experimental analysis of the surface morphology of the machined blade was carried out by the industrial robot abrasive grinding mechanism,and the correctness of the theoretical analysis and the effectiveness of the control strategy were verified.

基于Bouc-Wen前馈及模糊PID反馈的三层MFC-PBP并联变形翼大位移控制

三层预压缩压电宏纤维复合材料双晶片(MFC-PBP)并联变形翼存在较强的迟滞非线性,严重影响变形控制精度。为了实现对其的精确变形控制,本文提出了以Bouc-Wen逆模型为前馈补偿,以模糊PID作为反馈控制器的三层MFC-PBP并联变形翼的大位移跟踪控制策略。首先,利用遗传算法对Bouc-Wen模型进行参数识别,构建前馈模型并对变形翼进行开环控制;其次,加入模糊PID反馈控制器,得到可以自适应改变PID参数的闭环控制。变形跟踪控制试验结果表明,BoucWen前馈控制的相对误差(RE)约为13.5%,将Bouc-Wen前馈模型与模糊PID结合后控制的相对误差在4%以内,提高了变形控制精度。

基于PSO-Fuzzy PID的重载无人叉车液压马达同步控制策略

为保证重载无人叉车正常运行,减小对液压马达的同步控制偏差,提出一种基于粒子群优化的模糊PID方法(PSO-Fuzzy PID)的重载无人叉车液压马达同步控制策略。首先建立叉车液压马达的数学模型,其次基于叉车运行参数获得改进特征函数,利用特征值表示叉车运行状态,并作为PID方法、模糊PID方法和粒子群优化的模糊PID方法的输入。最后,相同条件下,通过比较三种不同方法下转速变化以及角度变化,验证了PSO-Fuzzy PID方法抗干扰能力好、调节能力佳。

Fuzzy self-tuning PID control of the operation temperatures in a two-staged membrane separation process

A two-staged membrane separation process for hydrogen recovery from refinery gases is introduced. The principle of the gas membrane separation process and the influence of the operation temperatures are analyzed. As the conventional PID controller is difficult to make the operation temperatures steady, a fuzzy self-tuning PID control algorithm is proposed. The application shows that the algorithm is effective, the operation temperatures of both stages can be controlled steadily, and the operation flexibility and adaptability of the hydrogen recovery unit are enhanced with safety. This study lays a foundation to optimize the control of the membrane separation process and thus ensure the membrane performance.

Fuzzy-GA PID controller with incomplete derivation and its application to intelligent bionic artificial leg

An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the geneticalgorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part andthe on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step re-sponse as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p , Ti* ,and Tj are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-linepart, based on K; , Ti* , and T*d and according to the current system error e and its time derivative, a dedicatedprogram is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mech-anism to ensure that the system response has optimal dynamic and steady-state performance. The controller has beenused to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computersimulation shows that this kind of optimal PID controller has excellent control performance and robust performance.