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PD-滑模耦合算法的压电智能薄板颤振抑制

针对工件柔性主导的薄壁件铣削颤振问题,提出了一种基于PD–滑模耦合算法的压电智能薄板颤振抑制方法。首先设计了只需位移测量的主动控制算法,基于滑模控制理论处理系统参数不确定性和外部扰动,通过动态补偿器对未知切削状态进行在线近似和补偿;为解决传感误差和系统时滞问题,在滑模控制器中进一步耦合了时空依变PD控制模型,借助ABAQUS仿真拟合控制参数时变函数;最后设计了一套薄壁件主动控制装置,试验结果显示采用主动控制可有效抑制薄壁件铣削颤振,验证了控制方法的可行性。

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。

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.

基于改进灰色预测单神经元PI的全超导托卡马克核聚变发电装置快控电源电流控制

全超导托卡马克核聚变发电装置(EAST)快控电源负载电感的电流受多种不确定环境因素的影响而难以预测,灰色预测无需精确对象模型,只需少量已知信息即可实现输出电流短期预测,已在EAST快控电源中有了一定研究应用。为解决灰色预测在EAST快控电源中对突变信号边沿预测精度低和预测延时时间长的问题,提出一种改进灰色预测算法实现输出电流预测。在一个开关周期内对输出电流进行等时长间隔采样4次作为原始序列,将滚动式采样预测改为逐周期采样预测,在实现灰色预测的过程中不必依赖过去几个周期的历史采样信息,只需本周期的4个原始采样值即可实现输出电流的预测。根据预测电流与参考电流误差自适应调整单神经元比例-积分(PI)控制的输出增益,实现输出电流的快速准确控制。仿真和实验结果表明,在EAST快控电源电流预测过程中所提出的改进灰色预测,对比传统灰色预测具有更高的预测精度和更小的预测延时,改进灰色预测变增益单神经元PI控制能够在减小电流超调的同时加快输出电流响应速度。

高压输电线软体巡检机器人磁致动机制及PI滑模控制研究

为解决高压输电线刚性悬臂巡检机器人尺寸大、笨重、越障不灵活、效率低等问题,设计一种高压磁致动软体巡检机器人,利用载流线圈在高压直流环形磁场中受到的安培力驱动软体机器人以柔顺和蠕动方式高效灵活地跨越多种障碍物。对该机器人进行本体构型设计并分析其工作姿态,实现稳定巡检功能;对磁致动驱动器进行理论设计和分析,利用Comsol仿真软件对其所需驱动力进行仿真,验证理论的可实行性;通过拉格朗日法对高压磁致动软体巡检机器人进行动力学建模,构建系统动力学方程,获取机器人的关节角度、速度、加速度之间的关系;最后,通过MATLAB利用基于PI滑模控制法对简化后的高压磁致动软体巡检机器人动力学模型实行速度和角速度高精度追踪仿真。仿真结果表明:在滑模控制下,角度在1.5 s追踪上理想信号,角速度在0.3 s后追上理想信号,证明基于PI的滑模控制器能够实现对高压磁致动软体巡检机器人的有效控制。

基于板带厚度误差PI反馈的轧机辊缝调节方法

针对轧机刚度系数未知波动影响板带厚度控制精度的问题,提出了基于板带厚度误差PI反馈的轧机辊缝调节方法。首先,建立轧机弹跳方程、液压辊缝调节动态方程,以及从辊缝设定值到板带出口厚度的动态方程;其次通过测厚仪和测压计,获取轧机出口处的板带厚度,以及轧机施加的轧制压力;然后设计基于板带厚度误差量PI反馈的辊缝大小的设定值,同时引入比例反馈增益系数与积分反馈增益系数的单参数化设计;最后将辊缝大小的设定值输入轧机液压辊缝调节系统进行辊缝调节,最终形成一套完整的轧机辊缝调节方法。该方法提高了轧制参数的预设定精度,大大减小了轧机刚度系数波动对板带厚度控制精度的影响,从而提高了板带厚度精度。

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。

基于串级PI-(1+PD)算法的含飞轮储能互联电网AGC控制器设计

针对传统控制器存在的响应速度较慢、超调较大及鲁棒性较差等问题,提出一种基于串级PI-(1+PD)算法的含飞轮储能互联电网AGC控制器设计方法。首先,建立含飞轮储能的两区域互联电网AGC系统模型,模拟飞轮储能联合火电机组参与AGC的过程。然后,设计一种基于串级PI-(1+PD)算法的AGC控制器。外环采用PI控制,内环采用带滤波系数的(1+PD)控制。在保证系统稳态性能的前提下,提高动态响应速度和抗扰能力,并通过粒子群算法的迭代寻优获得最优的控制器参数。最后,基于Matlab/Simulink进行算例仿真分析。结果表明:与传统PID控制和PI-PD控制相比,所提方法不仅具有更快的响应速度与更小的超调量,而且增强了系统抵御内部参数摄动的鲁棒性。

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.

Optimal PD/PID control of smart base isolated buildings equipped with piezoelectric friction dampers

Long-period pulses in near-field earthquakes lead to large displacements in the base of isolated structures.To dissipate energy in isolated structures using semi-active control,piezoelectric friction dampers（PFD） can be employed.The performance of a PFD is highly dependent on the strategy applied to adjust its contact force.In this paper,the seismic control of a benchmark isolated building equipped with PFD using PD/PID controllers is developed.Using genetic algorithms,these controllers are optimized to create a balance between the performance and robustness of the closed-loop structural system.One advantage of this technique is that the controller forces can easily be estimated.In addition,the structure is equipped with only a single sensor at the base floor to measure the base displacement.Considering seven pairs of earthquakes and nine performance indices,the performance of the closed-loop system is evaluated.Then,the results are compared with those given by two well-known methods：the maximum possive operation of piezoelectric friction dampers and LQG controllers.The simulation results show that the proposed controllers perform better than the others in terms of simultaneous reduction of floor acceleration and maximum displacement of the isolator.Moreover,they are able to reduce the displacement of the isolator systems for different earthquakes without losing the advantages of isolation.

基于PI闭环反馈的EtherCAT分布式时钟漂移补偿优化

接入工业总线的设备的增加会导致高延迟等同步性问题。基于EtherCAT对分布式时钟同步算法进行研究与分析,针对其在漂移补偿过程中存在补偿精度低的问题,提出了一种基于闭环反馈控制思想的动态漂移补偿算法。算法先利用定点数改进本地时钟,再利用PI控制调节漂移补偿值。最后搭建了测试平台,包括基于SOEM实现的主站与基于STM32+FPGA架构实现的从站,优化前后的算法分别在FPGA中实现。测试结果表明:定点数改进的本地时钟分辨率可以达到2^(-20) ns, PI闭环反馈优化后的漂移补偿算法可以将同步误差降低到25 ns之内,明显提高从站之间的同步性能。

基于滑模PI控制的电动汽车集群出力与调频联合优化

电动汽车(EV)集群在参与电网调频时,持续大量的出力可能会导致系统频率出现超调现象,且会增加系统的调频成本和对EV集群的补偿成本。在保证系统频率稳定的前提下,为了协调EV集群与发电机的出力,提出了基于滑模比例-积分(PI)控制的发电机和EV集群联合优化方法,并以系统调频成本和频率稳定为目标,对EV集群的车辆投入比例进行优化。考虑私家车、公交车、出租车这3种类型EV参与电网辅助调频,建立了EV集群参与微电网调频的单区域系统模型,通过滑模PI对EV集群进行控制,在保证频率稳定的情况下,显著减小EV集群出力;进一步对EV集群车辆投入比例参数进行优化,显著降低系统的调频成本。对发电机容量充足、不足的工况进行仿真,并对比参数优化前、后的结果,验证了所提联合优化方法的有效性和经济性。

基于NSGA-Ⅱ算法的ATR发动机PI控制器多目标优化方法研究

为了使空气涡轮火箭发动机(ATR)从慢车快速、稳定和准确地加速到最大状态,以ATR发动机燃气发生器流量和尾喷管喉部面积为控制变量,采用快速非支配排序遗传算法(NSGA-Ⅱ算法)建立了发动机控制器参数多目标优化方法。将超调量、稳态误差、上升时间及误差积分值四个指标以加权的形式作为目标函数,引入执行机构超调惩罚机制,建立了PI控制器参数Pareto最优解集,完成了ATR发动机从慢车加速到最大状态的动态过程仿真。结果表明,将双回路多个控制性能指标以加权的形式组合作为目标函数,可以获得均匀分布的Pareto前沿;联合应用多目标优化方法和基于熵权法的优劣解距离法(TOPSIS),能够在双回路耦合下获得满足设计要求的ATR发动机动态特性,极大地缩短了人工整定控制器参数的时间;在加速过程中,多目标优化方法将涡轮膨胀比上升时间作为目标函数之一,与尾喷管面积开环控制动态过程相比,可以使涡轮膨胀比更早到达目标值,共同工作线远离喘振边界。

Controllable growth of wafer-scale PdS and PdS_(2) nanofilms via chemical vapor deposition combined with an electron beam evaporation technique

Palladium(Pd)-based sulfides have triggered extensive interest due to their unique properties and potential applications in the fields of electronics and optoelectronics.However,the synthesis of large-scale uniform PdS and PdS_(2)nanofilms(NFs)remains an enormous challenge.In this work,2-inch wafer-scale PdS and PdS_(2) NFs with excellent stability can be controllably prepared via chemical vapor deposition combined with electron beam evaporation technique.The thickness of the pre-deposited Pd film and the sulfurization temperature are critical for the precise synthesis of PdS and PdS_(2) NFs.A corresponding growth mechanism has been proposed based on our experimental results and Gibbs free energy calculations.The electrical transport properties of PdS and PdS_(2) NFs were explored by conductive atomic force microscopy.Our findings have achieved the controllable growth of PdS and PdS_(2) NFs,which may provide a pathway to facilitate PdS and PdS_(2) based applications for next-generation high performance optoelectronic devices.

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.

含实时电流补偿的开关磁阻式机电作动器自适应PI控制

针对传统控制算法难以克服开关磁阻电机的非线性并有效抑制转向叶片的负载扰动,因而无法对开关磁阻式机电作动器进行快速高精度位置跟踪控制的问题,提出了一种含实时电流补偿的自适应PI控制器。首先,通过负载观测器对作用于转向叶片上的空气动力负载进行实时估算,进而转化为电流环的实时补偿电流来抑制负载扰动的影响;然后,基于开关磁阻电机的转矩电流特性拟合曲线与实时电流反馈值对转速环PI参数进行自适应最优调节,从而削弱了开关磁阻电机的非线性,增强了机电作动器的转速调节性能;最后,通过引入位置前馈环节,有效提高了机电作动器的响应速度和位置跟踪精度。仿真和实验结果表明:相较于传统PI控制器、自适应PI控制器和含前馈环节的自适应PI控制器,所提控制器将开关磁阻式机电作动器的响应速度分别提高了49.3%、44.4%和30%,位置跟踪精度分别提高了54.8%、49.2%和2.9%,从而使得机电作动器具备更强的位置跟踪性能。