MODIFIED AUTOMATIC TUNING OF PI/D CONTROLLERS BASED ON PHASE AND AMPLITUDE MARGIN SPECIFICATIONS

A new auto tuning procedure for PI/D controller based on phase and amplitude margin specifications was proposed.The procedure applied a modified relay feedback experiment to identifying the process frequency response of the point where the Nyquist curve intersects the negative imaginary axis,and the PI/D controller settings can be obtained based on this single point.The auto tuning method has all the merits of the tuning method that Astrom and Hagglund had proposed,and overcomes its problems.The simulation results show that the proposed tuning method has better performance than Astrom Hagglund’s tuning method.

基于非线性动态重心粒子群优化的分数阶PI^(λ)D^(μ)控制器设计

针对现有Oustaloup滤波器拟合精度不佳、结构复杂的缺点,提出了最优精简Oustaloup滤波器。针对粒子群优化算法整定分数阶PI^(λ)D^(μ)控制器参数时学习能力不充分、迭代收敛乏力的问题,提出了一种改进的粒子群优化算法。该算法设计了双异步非线性动态学习因子,以提高粒子的思考能力与信息共享能力,并增加了粒子群质量重心项,用以加速收敛过程。将改进的算法结合最优精简Oustaloup滤波器应用于分数阶PI^(λ)D^(μ)控制器的设计过程,选取了2个分数阶系统模型进行仿真验证。结果表明,改进的算法收敛速度更快且不易陷入局部最优,所设计的控制系统超调量更小、调节时间更短、稳态误差更小,提高了系统的抗干扰能力。

DAB储能变换器双闭环模型预测控制与PI补偿控制策略研究

针对直流微电网中分布式电源输入输出功率不稳定造成的直流母线电压波动问题,鉴于双有源桥(DAB)储能变换器具有能量双向流动的特点,提出一种基于内模原理的双闭环模型预测控制策略。在分析DAB变换器扩展移相调制原理与特性基础上,重点研究电压外环和电流内环的模型预测控制策略。针对模型预测控制性能依赖电路参数准确度问题,分别从电路主要参数失配对功率传输和直流母线稳压的影响两个角度出发,深入分析模型参数敏感度。最终设置直流母线电压稳态误差灵敏度区,在误差灵敏度区外仅采用双闭环模型预测控制以保证系统的动态响应性能,在误差灵敏度区内采用双闭环模型预测控制和PI补偿控制以消除系统稳态误差。搭建实验平台进行实验验证,结果表明:在系统受到扰动时,DAB储能变换器可实现快速地充放电对系统进行补偿,混合控制策略可快速保持母线电压的稳定,实现系统功率均衡,增强系统抗扰能力。

基于分数阶PID的火电厂烟气脱硝控制系统

针对选择性催化还原(SCR)烟气脱硝控制对象具有大迟延、大惯性的动态特性,采用常规的PID控制器经常不能实现及时和准确的喷氨控制。设计了基于分数阶PIλDμ控制器的火电厂烟气脱硝控制系统,并对人群搜索算法(SOA)的种群进行动态排序和随机变异而提出改进人群搜索算法,采用改进SOA算法对分数阶PIλDμ控制器的参数进行优化。仿真表明,分数阶PIλDμ烟气脱硝串级系统可以在保证控制精确度的前提下有效减少喷氨控制的调节时间,提高控制的稳定性,实现更加稳定和及时的喷氨控制。

基于DFFRLS的PMSM自校正PI速度控制策略研究

基于惯量辨识的永磁同步电机自校正PI速度控制具有良好的抗负载扰动性能,但受到惯量辨识过程存在抖动的影响转速响应会产生高频振荡。为抑制高频振荡,提出一种基于动态遗忘因子递推最小二乘法惯量辨识的自校正PI控制策略。首先,构造指数函数形式的动态遗忘因子,分析其跟随辨识误差变化的规律并用于转动惯量辨识。然后,采用“振荡指标法”设计PI参数整定公式,并结合DFFRLS惯量辨识过程进行自校正PI控制。仿真和实验结果表明:改进的DFFRLS有效减小了辨识惯量的抖动幅度;所提ST-PIC调速控制策略在保证转速高性能响应的同时有效抑制了高频振荡。

Vision Navigation Based PID Control for Line Tracking Robot

In a controlled indoor environment,line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots.A line tracking robot is a self-mobile machine that can recognize and track a painted line on thefloor.In general,the path is set and can be visible,such as a black line on a white surface with high contrasting colors.The robot’s path is marked by a distinct line or track,which the robot follows to move.Several scientific contributions from the disciplines of vision and control have been made to mobile robot vision-based navigation.Localization,automated map generation,autonomous navigation and path tracking is all becoming more frequent in vision applications.A visual navigation line tracking robot should detect the line with a camera using an image processing technique.The paper focuses on combining computer vision techniques with a proportional-integral-derivative(PID)control-ler for automatic steering and speed control.A prototype line tracking robot is used to evaluate the proposed control strategy.

基于PI3K/AKT/mTOR途径桔梗皂苷D对脑缺血再灌注诱导的血脑屏障的保护作用及机制研究

本研究旨在探讨桔梗皂苷D (Platycodin D,PLD)通过PI3K/AKT/mTOR途径对脑缺血再灌注诱导的血脑屏障的保护作用及机制研究。试验采用糖尿病Sprague-Dawley大鼠构建大脑中动脉闭塞模型。动物试验分组:sham组、HIR组、PLD组、3-MA组(3-甲基腺嘌呤,3-methyladenine)和PLD+3-MA组。分析各组大鼠神经功能缺损评分、脑含水量、脑梗塞体积和血脑屏障渗透性。透射电子显微镜观察血脑屏障超微结构变化和自噬。免疫印迹法分析紧密连接(Tight junction,TJ)蛋白(ZO-1、claudin-5和occludin)免疫荧光染色和蛋白质免疫印迹(Western blot,WB)法,分析自噬相关蛋白(Beclin-1、LC3B和p62)和PI3K/AKT/mTOR通路相关蛋白的表达。结果表明,PLD和自噬抑制剂3-MA治疗显著降低了脑梗死体积、脑含水量和伊文思蓝渗漏,同时增加了TJ蛋白的表达,降低了自噬相关蛋白的表达(P<0.05)。此外,PLD增加了PI3K、AKT和mTOR的磷酸化水平。PLD通过抑制自噬预防高血糖条件下,脑缺血再灌注损伤后的血脑屏障损伤,可能通过调节PI3K/AKT/mTOR信号通路来实现。

胰岛素联合大剂量维生素D治疗GDM对患者血清vaspin、Visfatin水平及妊娠结局的影响

目的:研究大剂量维生素D联合胰岛素对妊娠期糖尿病(GDM)孕妇血清内脏脂肪特异性丝氨酸蛋白酶抑制因子(vaspin)、内脏脂肪素(Visfatin)水平及妊娠结局的影响。方法:将本院2021年12月-2023年3月收治的GDM孕妇105例随机数字表法分为联合组(n=53例)与对照组(n=52例),除调整饮食、运动外,两组均接受门冬胰岛素治疗,联合组联合大剂量维生素D补充治疗,持续4周,比较治疗前、治疗4周后两组糖脂代谢、胰岛素抵抗、血25羟基维生素D3[25(OH)D3]、脂肪因子变化,均随访至妊娠结束,比较两组妊娠结局及不良反应。结果:治疗后,联合组空腹血糖(5.26±0.53 mmol/L)、餐后2h血糖(6.62±0.78 mmol/L)、糖化血红蛋白(5.12±0.6)%、总胆固醇(3.24±0.56 mmol/L)、甘油三酯(2.96±0.53 mmol/L)、低密度脂蛋白胆固醇(2.51±0.55 mmol/L)、空腹胰岛素(12.52±2.63 mU/L)、胰岛素抵抗指数(2.12±0.14)、vaspin(23.15±2.74 ng/ml)和Visfatin(102.56±20.27 ng/ml)均低于对照组,25(OH)D3(26.52±5.71 ng/ml)高于对照组(均P <0.05)。联合组自然分娩率(75.5%)高于对照组(55.8%),剖宫产率(15.1%)、巨大儿发生率(3.8%)均低于对照组(32.7%、15.4%)(均P<0.05);两组不良反应(7.5%比5.8%)无差异(P>0.05)。结论:胰岛素联合大剂量维生素D治疗GDM对患者调节糖脂代谢作用显著,推测其可通过下调vaspin、Visfatin,升高25(OH)D3水平,减轻胰岛素抵抗,改善妊娠结局。

基于Raspberry Pi及Drone Kit的无人机飞行控制教学实践应用

基于开源硬件Raspberry Pi,Pixhawk及开源软件Drone Kit,设计了高校无人机飞行控制教学实践课程。课程中使用Raspberry Pi作为无人机的机载计算机与Pixhawk飞控芯片协同工作,通过使用MAVLink协议通信。学生在实践操作中需要掌握硬件各接口的功能属性进行无人机组装,在调测环境下,基于Drone Kit开发无人机飞行控制程序。所提的实践课程可以提高学生的动手实践能力,新环境下的创新应用能力能加深对飞行控制技术的理论理解。

Construction Cost Control Strategy of Finished House Based on BIM-5D

Building Information Modeling(BIM)technology can not only achieve project refinement management of the construction process based on a 3D model but also form a BIM-5D model based on the fusion of progress data and cost data to achieve the dynamic control of the whole process of project cost.This paper discusses the application of BIM-5D in the cost control of finished house construction,aiming to provide technical reference for China’s finished house project units,so that they can master how to realize the construction of cost control system based on BIM,the monitoring of the key coefficients of the cost,corrective action,to ensure that the project cost deviation is within the controllable range,and to make an effective guarantee for the economic benefits of the project construction.

基于模型变结构PI的BLDC电机驱动液压泵的压力控制

在自动变速箱液压控制的应用中,滑阀板液压系统存在控制精度差、多参数耦合、系统模型难以建立等缺陷。文章提出基于模型变结构比例积分(PI)的直流无刷(BLDC)电机驱动液压泵的压力控制策略,将直流无刷电机动态特性好、控制精度高等优点与液压泵结构简单、可靠性好等特点相结合。根据液压泵的转速、流量、压力特性关系和油液温度、黏度、弹性模量等参数建立电机转速比例积分控制模型,通过较易控制的电机驱动占空比信号实现对液压泵输出压力的调节。控制系统通过变结构比例积分控制,消除压力偏差和超调量,提高系统精度。仿真结果表明,变结构比例积分控制具有可行性,而试验结果证明,模型控制的系统响应优于纯比例积分闭环控制,并且具有很好的抗负载扰动能力。

Hybrid Optimized PI Controller Design for Grid Tied PV Based Electric Vehicle

Nowadays,researchers are becoming increasingly concerned about developing a highly efficient emission free transportation and energy generation system for addressing the pressing issue of environmental crisis in the form of pollution and climate change.The introduction of Electric Vehicles(EVs)solves the challenge of emission-free transportation while the necessity for decarbonized energy production is fulfilled by the installation and expansion of solar-powered Photovoltaic(PV)systems.Hence,this paper focuses on designing an effective PV based EV charging system that aids in stepping towards the achievement of a pollution free future.For overcoming the inherent intermittency associated with PV,a novel DC-DC converter is designed by integrating both Trans Z-source con-verter and Luo converter,which offers remarkable benefits of high conversion range,lesser voltage stress and excellent efficiency.A novel robust Lion Grey Wolf Optimized Proportional Integral(LGWO-PI)controller is designed for sig-nificantly strengthening the operation of the integrated converter in terms of peak overshoot,Total Harmonic Distortion(THD)and settling time.A 3’Voltage Source Inverter(VSI)is employed to convert the stable DC output from the PV sys-tem to AC,which is then used for driving the Brushless Direct Current Motor(BLDC)motor of EV.The speed of the BLDC is regulated using a PI controller.The BLDC motor gets the power supply from the grid during the unavailability of PV based power supply.The grid is integrated with the designed EV charging system through a 1’VSI and the process of grid voltage synchronization is carried out with the application of PI controller.The simulation for evaluating the operation of the presented EV charging system is done using MATLAB and the attained out-comes have validated that this introduced methodology delivers enhanced perfor-mance with optimal efficiency of 97.6%and lesser THD of 2.1%.

Performance of the Boost Chopper, Comparative Study between PI Control and Neural Control to Regulate Its Output Voltage

In this study, we investigate the performance of a boost converter regulating its output voltage using two control methods: Proportional-Integral (PI) control and neural control. Both methods are implemented on a simulation platform (Matlab/Simulink) and evaluated in terms of accuracy, response speed, and robustness to disturbances. Indeed, the output voltage of converters exhibits imperfections that require a control method to optimize efficiency when applying a variable load. Results show that neural control offers superior performance in terms of accuracy and response time, with faster and more precise regulation of the output voltage. On the other hand, PI control proves to be more robust against disturbances. These findings can help guide the selection of the appropriate control method for a boost converter based on the specific requirements of each application.

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit.Renewable energy sources are playing a significant role in the modern energy system with rapid development.In renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental changes.To develop energy resources,electric power generation involved huge awareness.The power and output voltages are plays important role in our work but it not considered in the existing system.For considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy system.The input information is collected from two input sources.After that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model.Hidden layer 1 is transferred to hidden layer 2.Gaussian activation is employed for determining the output voltage with help of the controller.At last,the output layer offers the last value in GPIC-MDCNNC Model.The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages.GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works.The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

DC–DC Converter with Pi Controller for BLDC Motor Fuzzy Drive System

The Brushless DC Motor drive systems are used widely with renewable energy resources.The power converter controlling technique increases the performance by novel techniques and algorithms.Conventional approaches are mostly focused on buck converter,Fuzzy logic control with various switching activity.In this proposed research work,the QPSO(Quantum Particle Swarm Optimization algorithm)is used on the switching state of converter from the generation unit of solar module.Through the duty cycle pulse from optimization function,the MOSFET(Metal-Oxide-Semiconductor Field-Effect Transistor)of the Boost converter gets switched when BLDC(Brushless Direct Current Motor)motor drive system requires power.Voltage Source three phase inverter and Boost converter is controlled by proportional-integral(PI)controller.Based on the BLDC drive,the load utilized from the solar generating module.Experimental results analyzed every module of the proposed grid system,which are solar generation utilizes the irradiance and temperature depends on this the Photovoltaics(PV)power is generated and the QPSO with Duty cycle switching state is determined.The Boost converter module is boost stage based on generation and load is obtained.Single Ended Primary Inductor Converter(SEPIC)and Zeta converter model is compared with the proposed logic;the proposed boost converter achieves the results.Three phase inverter control,PI,and BLDC motor drive results.Thus the proposed grid model is constructed to obtain the better performance results than most recent literatures.Overall design model is done by using MATLAB/Simulink 2020a.

基于PI参数的二阶线性自抗扰控制参数整定

线性自抗扰控制是一种不依赖于被控过程模型的控制方法,具有很好的工程应用潜力。目前,工业控制过程中大多采用PI控制器,为了确保由调试好的PI控制器平稳切换到线性自抗扰控制器,并使系统仍保持稳定状态,需要合理设置线性自抗扰控制器的参数。为此,在分析二阶线性自抗扰控制器的二自由度等效结构的基础上,推导出其反馈控制器与PID控制器的对应关系,给出一种基于现有PI控制参数直接获取二阶线性自抗扰控制初始参数的方法。最后,在MATLAB/Simulink平台上采用若干典型传递函数和双容水箱液位控制系统进行仿真研究,仿真结果验证了所提方法的有效性。

基于PI建模和反步滑模控制的主动波浪补偿策略

海上起重船受风、浪、涌影响会产生剧烈的船舶姿态变化,造成起重机和货物的位姿变化,对货物和人员存在安全隐患,波浪补偿平台的稳定性控制能有效减少复杂海况下船舶运动对海上作业安全性、稳定性和精准性的影响,对浮式起重船海上设备精准装载作业极其重要。针对补偿平台的迟滞非线性导致的建模困难和控制不精确问题,本文提出基于PI(Prandtle–Ishlinskii)建模和反步滑模控制的主动波浪补偿策略。首先,通过实验得到补偿系统的迟滞效应曲线,分析系统迟滞环建立PI迟滞模型,并采用递推最小二乘法辨识模型的各个参数,从而求得系统模型。然后,基于李雅普诺夫(Lyapunov)稳定性设计反步控制补偿方法,并结合滑模控制规律加快初始控制速度。最后,将反步滑模法应用于补偿系统,采用MATLAB软件仿真在规则波和不规则波下的响应来验证算法和模型的正确性,并在工控机中用C#编写控制程序,驱动运动控制卡控制伺服电机带动电缸进行补偿运动,同时通过传感器采集系统运动的实时数据,并反馈给工控机形成闭环,以期验证补偿平台在补偿规则波和不规则波下的补偿效果。实验结果表明,所建立的斯图尔特(Stewart)浮式平台中,PI迟滞模型具有良好的精度,反步终端滑模控制算法在Stewart平台的实际控制中能够很好地补偿波浪运动,相比比例–积分–微分控制(PID)、反步法、强化学习等控制方法,反步终端滑模方法能快速较好跟踪期望位移,补偿精度达到0.9729。

基于D-分割法的直流变换器遗传自抗扰控制器设计

针对光伏发电系统中DC-DC变换器由于负载和工作环境条件变化等扰动引起的输出波动问题,提出一种基于D-分割法的直流变换器遗传自抗扰控制器(ADRC)设计方法。该方法适用于设计光伏发电领域中的双向DC-DC变换器,采用D-分割法获得满足闭环系统鲁棒稳定的ADRC控制器参数范围;利用具有全局寻优能力的遗传算法,按综合性能指标在该范围内进行参数寻优。实验结果表明,所提基于D-分割法的直流变换器遗传自抗扰控制器设计方法能有效抑制微网母线侧的电压波动和负载突变,提高控制器的鲁棒性,增强光伏发电系统的动态响应性能和抗干扰能力。

基于SVPWM的PMSM模糊PI-DTC控制研究

为了解决在直接转矩控制(DTC)下永磁同步电机(PMSM)出现的转矩波动大、定子磁链和电流失真严重等问题,提出基于空间电压矢量控制的改进型PMSM模糊PI-DTC控制方法。用电压空间矢量脉宽调制(SVPWM)代替传统DTC的开关表搜索模块,并对PI调节器引入模糊规则在线整定参数,从而得到合适的空间电压矢量,进一步改善电机性能。仿真结果表明:基于SVPWM的PMSM模糊PI-DTC控制技术使得电机启动转矩增大到28.97 N·m,电机启动速度更快,上升时刻缩短,转速跟踪的超调量减为0.51%,极大地提高了跟踪精度,并能显著改善电流畸变和抑制转矩脉动。

模糊整定PI参数的双柔性机械臂振动抑制

机械臂伺服驱动系统中柔性因素的存在易导致机械臂输出转速波动,甚至引发系统谐振.为抑制系统转速波动,使机械臂传动系统性能稳定,使用模糊理论整定PI控制器参数的控制策略.根据假设模态法和拉格朗日动力学方程建立了考虑LuGre摩擦模型的双柔性机械臂传动系统动力学方程,并分析了动力学方程中耦合非线性项对系统传动特性的影响.使用极点配置策略确定PI控制器参数的取值范围,后根据模糊规则实时调整控制器参数,以减小伺服系统输出转速的波动,进而抑制系统谐振.最后,借助数值仿真分析和机械臂控制实验,与传统PI控制策略对比,发现本文所述控制策略可使电机端转角跟踪误差绝对值的平均值降低43.066%,柔性负载转角误差的标准差降低46.506%,更加验证了所提模糊规则整定PI控制器参数抑振方法的有效性.