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.

Fuzzy Self-adaptive Proportional Integration Differential Control for Attitude Stabilization of Quadrotor UAV

The technology of attitude control for quadrotor unmanned aerial vehicles(UAVs) is one of the most important UAVs' research areas.In order to achieve a satisfactory operation in quadrotor UAVs having proportional integration differential(PID) controllers,it is necessary to appropriately adjust the controller coefficients which are dependent on dynamic parameters of the quadrotor UAV and any changes in parameters and conditions could affect desired performance of the controller.In this paper,combining with PID control and fuzzy logic control,a kind of fuzzy self-adaptive PID control algorithm for attitude stabilization of the quadrotor UAV was put forward.Firstly,the nonlinear model of six degrees of freedom(6-DOF) for quadrotor UAV is established.Secondly,for obtaining the attitude of quadrotor,attitude data fusion using complementary filtering is applied to improving the measurement accuracy and dynamic performance.Finally,the attitude stabilization control simulation model of the quadrotor UAV is build,and the self-adaptive fuzzy parameter tuning rules for PID attitude controller are given,so as to realize the online self-tuning of the controller parameters.Simulation results show that comparing with the conventional PID controller,this attitude control algorithm of fuzzy self-adaptive PID has a better dynamic response performance.

Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.

Fractional Order Proportional Integral Derivative Controller Design and Simulation for Bioengineering Systems

This paper deals with the study of fractional order system tuning method based on Factional Order Proportional Integral Derivative( FOPID) controller in allusion to the nonlinear characteristics and fractional order mathematical model of bioengineering systems. The main contents include the design of FOPID controller and the simulation for bioengineering systems. The simulation results show that the tuning method of fractional order system based on the FOPID controller outperforms the fractional order system based on Fractional Order Proportional Integral( FOPI) controller. As it can enhance control character and improve the robustness of the system.

Adaptive proportional integral differential control based on radial basis function neural network identification of a two-degree-of-freedom closed-chain robot

A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.

基于改进麻雀优化PID的波浪补偿控制方法

随着海上风电“十四五”规划的不断推动,在深远海域对兆瓦级大功率海上风机的需求量随之增加,其规模也在不断扩大。但是,在吊运、安装等海上工作过程中,复杂海浪对船舶产生的持续影响导致风机安装的精度和效率大幅下降,甚至会对人员安全以及财产造成重大损失。在深远海域复杂海况下对工程船舶进行有效的波浪补偿,可提供稳定的作业环境以保证精准高效地完成各项工作,因此,本文提出了一种基于改进麻雀优化PID的波浪补偿控制方法并将其应用于Stewart补偿平台。首先,建立波浪补偿平台动力学以及运动学反解模型,并设计正解模型迭代求解算法。随后,使用PID进行波浪补偿控制,并通过麻雀搜索算法优化参数。接着,采用Circle混沌映射对其进行初始化分布,以解决初始化不均匀的问题;并采用动态自适应加权、柯西突变以及反向学习以提升算法全局寻优能力。最后,生成4~6级海况下的某工程船运动数据作为系统输入,利用MATLAB和Simulink软件平台搭建模型进行补偿控制验证,并在Stewart硬件平台上做补偿试验。结果表明,改进麻雀搜索算法具有较快的收敛速度、较高的精度和更好的寻优能力,优化后的PID控制方法更适合用于复杂海况下波浪补偿平台的控制优化,可为大功率海上风机安装的补偿平台控制系统设计提供参考。

数控压机伺服控制系统复合控制器I-ABC与PID优化

为了提高数控压机伺服控制系统的控制精度,针对伺服控制系统运行控制过程构建数学模型,在人工蜂群算法基础上融入了云分析模型,之后采用改进人工蜂群算法(Improved Artificial Bee Colony,I-ABC)调节比例-积分-微分(Proportional Integral Differential,PID)参数,建立了一种复合控制方法。研究结果表明:以I-ABC进行PID控制时,可以使幅度差降低到0.4%,相位差基本在-0.54°之内,系统加载精度也获得了明显提升,表现出了更优的跟踪性能。以I-ABC进行PID控制时,能够对多余力起到明显抑制作用,响应速度也获得明显提升,可以有效满足系统的准确控制要求。在系统内加入干扰信号,引入I-ABC实施PID调节可以减小系统超调量,还可以获得更短调节时间,使系统获得更强抗干扰性能。

有源电力滤波器补偿的PI解耦控制技术研究

电网中电压和电流波形的失真会产生谐波,导致电网功率因数降低,并影响电力系统的稳定性和可靠性。为了确保电力系统的正常工作,研究了基于有源电力滤波器补偿的比例积分(PI)解耦控制技术。设计了基于二极管钳位式的有源电力滤波器模型。结合基尔霍博电压法,降低控制谐波和解除部分变量耦合。针对设备非线性产生的谐波,分析有源电力滤波器的可逆性,获得输入雅可比矩阵,以实现滤波器旋转坐标的线性化。利用变换矩阵转换转动角速向量,输入谐波的幅度,输出电流取样延迟,完成无差PI解耦控制。试验结果表明,所提技术能够有效解耦滤波器的谐波电流成分,从而补偿电网中的失真电流。该技术可以减少谐波对电力系统的整体影响,保证设备的正常运行。

基于自适应PI控制的superbuck变换器

针对superbuck变换器稳定性易受参数偏移和工况变化影响的问题,提出了一种基于瞬态事件捕获的自适应比例积分(PI)控制方法。在该方法中,事件捕获电路通过计算瞬态响应中输出电压的上冲和下冲次数,根据振荡周期把电压响应分类成不同的瞬态事件。更进一步,对不同的瞬态事件进行加权计算得到PI控制器中的增益,从而调节系统带宽和相位裕度。在阻尼网络发生参数偏移的情况下,当参考电压从25 V变化到28 V时,振荡最大幅度从2 V减小到400 mV。100 W superbuck变换器的实验结果表明,自适应PI控制器在参数偏移和工况变化下有效地改善稳定性。

基于比例谐振自耦PI控制的调速系统

针对永磁同步电机调速系统矢量控制过程中,逆变器死区、电机磁场分布谐波以及电流在采样过程中存在误差等因素导致的电机控制效果不佳问题,提出了一种改进型自耦PI控制的双环控制策略。为减小信号的跟踪误差,采用加速度前馈的方式,将增加Levant微分器的自耦PI控制器引入到永磁同步电机速度环中,确保对含有噪声的输入信号准确求导以提高系统的鲁棒性。另外,所提策略在电流环中将增加加速前馈的自耦PI控制结构的比例项和标准谐振器相结合,构成比例谐振自耦PI控制器,使永磁同步电机在电流环的控制中能更好地补偿电流,达到抑制电流谐波的作用。通过分析伺服系统的控制机理,在被控对象PMSM数学模型的基础上,给出了改进型自耦PI控制结构的设计方法。实验结果表明,所提出的控制策略能够减小电机自身参数变化对系统性能的影响并且在低速域具有良好的抑制电流谐波作用,验证了所设计的改进型自耦PI控制器的有效性。

基于改进MPC与RBF-PID的智能车轨迹跟踪控制

为提升智能汽车轨迹跟踪控制的稳定性和精度,提出一种基于时域参数自适应MPC与RBF-PID的轨迹跟踪控制方法.首先搭建车辆横纵向动力学模型;其次分析控制时域与预测时域对跟踪精度的影响,设计时域参数自适应MPC横向控制器并进行仿真验证;然后基于径向基函数(RBF)神经网络整定PID控制参数,设计分层式纵向控制器,并设计折线速度曲线与PID算法进行对比;最后以车速为耦合点构建智能汽车横纵向综合控制系统,并在蛇形工况下对横纵向综合控制系统进行仿真实验.结果表明,所设计的横向控制器能保证低速时的跟踪精度和高速时的车辆稳定性,纵向控制器可有效提高速度跟踪精度,横纵向综合控制系统能实现车辆在变车速工况下对轨迹的精准跟踪,同时保证良好的行驶稳定性与舒适性.

基于神经网络PI的电静液作动器温度补偿控制方法研究

电静液作动器(Electro-hydrostatic Actuator,EHA)作为一种高性能集成化的动力执行器,广泛应用于航天航空、工业生产和军事领域。在不同油液温度下,液压油的体积弹性模量、黏度等性质会发生变化,从而对EHA的控制精度产生影响。针对此问题,首先建立了EHA数学模型;其次,设计了反向传播(BP)神经网络对比例积分控制器(PI)控制参数进行在线调节,补偿温度变化对跟踪精度的影响;最后,通过实验验证了传统PI控制的EHA跟踪精度与温度变化有着密切关系,并将BPPI控制器与PI控制器进行实验对比。实验结果表明,所设计的BPPI控制器在不同油液温度下平均跟踪误差之间的差值较PI控制器降低80%以上;且在相同温度下,平均跟踪误差较PI控制器降低60%以上。

全桥LLC谐振变换器的积分滑模-PI混合控制

全桥LLC谐振变换器的PI控制存在响应慢和鲁棒性差的问题,滑模控制存在高频抖振问题。针对这些问题,提出了基于模糊规则切换的积分滑模-PI混合控制方法。首先,通过扩展函数描述法建立全桥LLC谐振变换器的大信号模型,利用输入输出反馈线性化设计滑模面;然后,利用扫频法验证PI控制设计的合理性;最后,经多次实验,确定模糊控制的最佳输入和输出隶属度函数,实现在瞬态时由滑模控制起主导作用,在稳态时仅使用PI控制的混合控制方法。在MATLAB/Simulink中进行仿真分析,仿真结果表明,所提控制方法不仅具有滑模控制的瞬态响应快、鲁棒性强的优点,还消除了高频抖振,具有良好的控制效果。

基于改进DDPG-PID的芯片共晶键合温度控制

芯片共晶键合对加热过程中的升温速率、保温时间和温度精度要求较高,在使用传统的比例-积分-微分(PID)温度控制方法时,存在响应时间过长、超调量过大、控制温度不够准确等问题。针对共晶加热台的温度控制问题,提出了一种基于改进的深度确定性策略梯度(DDPG)强化学习算法优化PID参数的控制方法,采用分类经验回放的思想,以奖励值大小为标准对经验进行分类存放,根据智能体当前的状态和下一步动作,从相应的经验池中进行采样并训练,并根据PID控制算法的特性设计了合理的奖励函数,改善了强化学习中奖励稀疏的问题,提高了算法的收敛速度与性能。仿真结果表明,与传统PID控制、常规DDPG-PID控制相比,改进DDPG-PID控制缩短了响应时间,降低了超调量,近乎消除了稳态误差,提高了控制性能和系统稳定性。

基于MPC和PID的脚轮式全向移动平台轨迹跟踪

针对现有运动控制策略无法保证独立驱动脚轮式全向移动平台位姿的高精度控制问题,提出一种基于模型预测控制(MPC)和PID控制相结合的双闭环轨迹跟踪控制策略。首先,利用运动学几何关系建立独立驱动脚轮式全向移动平台在世界坐标系下的三自由度运动学模型,基于正交分解法建立平台在机器人坐标系下的逆运动学模型,以反映平台中心点速度与各个脚轮转速间的关系;其次,采用MPC并基于三自由度运动学模型设计位姿控制器,使平台对期望轨迹进行位姿跟踪,并在考虑多目标约束条件的情况下通过位姿控制器求解出最优控制量;最后,采用PID设计速度控制器,用于跟踪位姿控制器输出的期望速度,通过平台逆运动学模型计算得到期望轮速,从而驱动平台实现全向运动。通过仿真验证了所提控制策略的有效性,平台能有效跟踪直线轨迹和圆形轨迹。仿真结果表明,与通过平台转角逆运动学模型解耦驱动轮速的位置单环轨迹跟踪控制策略相比,加入速度内环后系统超调量下降97.23%,响应时间缩短36.84%。

Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

An autonomous microgrid that runs on renewable energy sources is presented in this article.It has a supercon-ducting magnetic energy storage(SMES)device,wind energy-producing devices,and an energy storage battery.However,because such microgrids are nonlinear and the energy they create varies with time,controlling and managing the energy inside them is a difficult issue.Fractional-order proportional integral(FOPI)controller is recommended for the current research to enhance a standalone microgrid’s energy management and performance.The suggested dedicated control for the SMES comprises two loops:the outer loop,which uses the FOPI to regulate the DC-link voltage,and the inner loop,responsible for regulating the SMES current,is constructed using the intelligent FOPI(iFOPI).The FOPI+iFOPI parameters are best developed using the dandelion optimizer(DO)approach to achieve the optimum performance.The suggested FOPI+iFOPI controller’s performance is contrasted with a conventional PI controller for variations in wind speed and microgrid load.The optimal FOPI+iFOPI controller manages the voltage and frequency of the load.The behavior of the microgrid as a reaction to step changes in load and wind speed was measured using the proposed controller.MATLAB simulations were used to evaluate the recommended system’s performance.The results of the simulations showed that throughout all interruptions,the recommended microgrid provided the load with AC power with a constant amplitude and frequency.In addition,the required load demand was accurately reduced.Furthermore,the microgrid functioned incredibly well despite SMES and varying wind speeds.Results obtained under identical conditions were compared with and without the best FOPI+iFOPI controller.When utilizing the optimal FOPI+iFOPI controller with SMES,it was found that the microgrid performed better than the microgrid without SMES.

基于改进麻雀搜索算法分数阶PI的PMSM调速策略

永磁同步电机调速系统采用传统比例积分双闭环控制结构存在抗扰性能差与响应速度慢的问题。为此,文中在建立PMSM数学模型和分析传统PI参数设计方法的基础上,提出一种基于改进麻雀搜索算法分数阶比例积分的PMSM调速优化策略,通过在常规SSA中引入佳点集初始化策略和黄金正弦更新策略,解决了SSA易陷入局部最优的问题,提高了收敛速度和精度,以进一步改善PMSM调速系统的响应性能。最后通过MATLAB仿真对比结果可知,所述ISSA-FOPI调速改进策略在4种工况下具有最短的调节时间分别为1.2、0.9、9.9与12.1 ms,和最小的超调量分别为0.16%、0.16%、0.05%与0.6%,这充分体现了所述策略的有效性与优越性。

永磁同步电机抗积分饱和PI控制研究

永磁同步电机矢量控制中传统PI控制器在给定输入发生较大幅度变化造成积分项逐渐累加,引起积分饱和,从而使转速及转矩超调,系统调节变缓,控制系统控制性能降低。针对这一问题,在传统PI控制基础上提出了一种抗积分饱和PI控制控制策略,将该控制策略分别应用到永磁同步电机速度环和电流环PI控制中。为验证该控制策略的有效性,基于MATLAB/Simulink仿真平台构建了永磁同步电机抗积分饱和PI控制系统仿真模型。仿真实验结果表明,与传统的PI控制算法相比较,所构建抗积分PI控制模型系统超调量明显降低,能够快速到达稳态,具有较好的动态性能和抗干扰能力。

基于局部时空的多峰优化算法及其在PID控制中的应用

多峰优化问题(MultiModal Optimization Problems,MMOPs)需要同时找到问题的多个高精度全局最优解,它需要算法具有较强的全局搜索能力且能很好地平衡种群的多样性和收敛性.当前在处理MMOPs时通常面临以下难点:(1)现有方法通常只考虑到进化过程中种群的当前状态(如常用的贪婪选择策略),容易导致种群陷入局部最优;(2)传统的随机搜索策略在复杂搜索空间内难以快速有效找到全局最优解;(3)当前设计的多峰优化算法往往需要手工设置参数(如变异因子和交叉因子等),而参数的大小将直接影响种群的多样性和收敛性.针对上述难点,本文提出了一种新的基于局部时空的多峰优化(Localized Time-Distance-based Multimodal Optimization,LTDMO)算法,主要包括三个贡献点:首先,提出了结合随机搜索和定向引导的变异(Random and Direction-based Mutation,RDM)策略,利用随机变异增加种群中个体的多样性,并通过划分邻域将整个种群分成不同的可重叠子种群,在局部搜索空间内进行变异操作来更好地定位全局最优解,从而避免个体陷入局部最优.其次,提出了基于时间局部性原理的拥挤选择(Locality-based Crowding Selection,LCS)策略,利用进化过程中的时间局部性记录对当前个体更有潜力的进化方向,并在此方向上生成新的子代,使种群进一步向全局最优解收敛.最后,提出了自适应参数控制(Self-adaptive Parameter Control,SPC)策略,基于个体进化信息自适应调整算法的参数值,降低算法在进化过程中对变异因子和交叉因子的参数敏感性.本文将LTDMO算法在CEC'2013测试集上进行实验,并将结果与其他11种多峰优化算法对比,表明LTDMO算法能有效处理较多的全局最优复杂多峰优化问题,具体地,在F1~F5、F8和F10问题上峰值率和成功率均达到100%;在具有较多局部最优的多峰优化问题(F6和F7)上,LTDMO算法的峰值率达到86%以上,这优于9种其他对比算法的性能;在处理复合多峰优化问题时,LTDMO算法在处理F11、F12、F14、F16问题上性能达到最优.同时将LTDMO算法在比例-积分-微分(Proportional-Integral-Derivative,PID)控制器上进行应用,结果表明LTDMO算法能为PID控制器找到多种最优控制参数,使系统达到稳定状态且误差更小.

非理想条件下基于SAADR-PI的MMC型电力电子变压器CCS-MPC控制策略

基于模块化多电平换流器(modular multilevel converter,MMC)的电力电子变压器(power electronic transformer,PET)在非理想工况下易发生故障及扰动,严重影响系统电能质量。针对传统控制方法在非理想工况下存在的动稳态性能差等问题,在MMC-PET整流级提出了基于自适应自抗扰比例积分控制器的连续控制集模型预测控制策略。首先,设计了自适应自抗扰比例积分控制器用于电压外环,解决了电压外环信号跟踪及扰动抑制能力差等问题。其次,电流内环使用连续控制集模型预测控制方法以提高系统的响应速度及稳态性能,引入改进型载波移相调制策略解决桥臂电流畸变问题。最后,在网侧负载突变、网压不平衡、输出级负载投入等非理想工况下对MMC-PET系统进行对比仿真和实验,验证了所提控制策略的优越性。