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.

Improvement of one-cycle controller by use of proportional integral differential controller

The main advantage of one-cycle control is its ability to reject input disturbance in one-cycle. Despite this great ability, it can not provide good responses in following commands and rejecting load disturbance. This study explores the way to overcome these problems by using another controller. Although the idea of using output feedback has been used in previous works, by considering a simple model for one-cycle controller, the design of the controller has become simpler in this work. In the proposed method, difficult mathematical modeling is avoided. Based on decupling of effects of feedback and input voltage disturbance, a simple model for one-cycle controller has been given. Therefore, by employing a conventional averaging method and the model of one-cycle controller, design of proportional integral differential controller has become straightforward.

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.

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。

引入PID反馈的SHAEKF算法估算电池SOC

电池荷电状态(SOC)的估算精度是电动汽车电池组的重要指标。为提升SOC估算精度,在融合Sage-Husa扩展卡尔曼滤波(SHEKF)算法与自适应扩展卡尔曼滤波(AEKF)算法的基础上,增加比例积分微分(PID)反馈环节,形成改进算法。采用粒子群优化(PSO)算法对二阶RC等效电路模型进行参数辨识;用开源电池数据集对模型和算法进行实验和分析。改进的SHAEKF算法在电池动态应力测试(DST)、北京动态应力测试(BJDST)和美国联邦城市驾驶(FUDS)等工况下的平均估计误差都在1%以内,与单纯的融合算法SHAEKF算法相比,最大误差可减小5%。

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。

基于金豺优化PID的直流电机调速控制系统

针对传统直流(DC)有刷电机调速系统适应性不强、抗干扰能力和稳健性差等缺点,设计了基于金豺算法优化比例-积分-微分(PID)的直流有刷电机控制系统.在建立电机数学模型的基础上,将金豺优化(GJO)算法应用于PID参数整定,实现对电机的控制,并分别与传统试凑法、反向传播(BP)-PID算法和麻雀算法(SSA)-PID进行对比.仿真结果表明:所设计的控制算法在调节时间上减少了12 ms,超调量降低了3.689%,受到干扰后的调节时间减少了17 ms,表现出更快的调节转速、更强的抗干扰能力和更好的稳健性,为直流有刷电机调速控制提供了一种有效的方案.

基于APID-RBF神经网络的光伏MPPT方法

针对光照强度急速变化和局部阴影时光伏发电系统最大功率点追踪响应速度慢、多峰值等问题,提出一种基于RBF神经网络与自适应PID控制相结合的控制方法。首先,采用RBF神经网络对环境的实时变化直接跟踪光伏最大功率点。然后,利用自适应PID的辅助修正,抑制光伏电池输出功率的波动。神经网络能提升在复杂环境下的跟踪速度,自适应PID能增强对神经网络误差的消除能力,提升跟踪精度。仿真结果表明,APIDRBF双控策略具有稳态性能高和控制精度高等优点,能有效提高光伏发电效率和稳定性。

基于模糊PID的打磨头角度控制策略研究

在进行风电机组叶片表面打磨工序时,需要控制打磨头的径向与叶片表面保持垂直。对打磨头的角度调整问题进行分析和研究,通过分析打磨头的结构构成与运动轨迹,建立打磨头伺服系统的数学模型。基于角度变化引起的重力转矩补偿量,提出一种模糊比例-积分-微分(Proportion-Integral-Differential,PID)的角度伺服控制系统。Simulink仿真验证结果表明,重力补偿后模糊PID控制方法的打磨头伺服系统响应速度更快、误差更小,能够较好地调整打磨头的角度。

基于模糊神经网络PID的无人艇航向控制器研究

针对常规比例、积分和微分(proportional integral derivative,PID)控制器在无人艇航向控制系统中表现出的稳定性差、控制精度低等问题,文章提出一种将模糊控制与反向传播(back propagation,BP)神经网络相结合的控制算法;在MATLAB中对比常规PID控制器、模糊PID控制器与模糊神经网络PID控制器在给定期望航向角下的航向控制性能,仿真结果表明模糊神经网络PID控制器对无人艇的航向控制性能最佳;在搭建的实验平台上对不同航向控制器下无人艇的航行轨迹和航向角进行比较,实验结果进一步验证了模糊神经网络PID航向控制算法的优越性。

一种联合PID控制与扩展卡尔曼滤波的磷酸铁锂电池荷电状态估算方法

锂离子电池作为新能源存储的载体,是执行“双碳”目标的重要助力,精确估算电池荷电状态(state of charge,SOC)能够有效辅助电池管理,进而延长电池使用寿命。针对卡尔曼滤波类算法的SOC估算效果受磷酸铁锂电池特性制约的问题,该文提出一种比例积分微分(proportional integral differential,PID)控制与扩展卡尔曼滤波(extended Kalman filter,EKF)联合方法。该方法利用PID控制原理设计SOC初值补偿策略并优化EKF算法的状态变量修正过程,可降低磷酸铁锂电池特性对算法的影响。实验结果表明,与EKF算法相比,所提方法在估算磷酸铁锂电池SOC时拥有更高的估算精度与更快的收敛速度,对电池模型误差与采样噪声表现出较强的鲁棒性。

静止无功补偿器的小波神经网络PID控制方法研究

静止无功补偿器(SVC)是电力系统中应用广泛的动态无功补偿装置。针对传统比例积分微分(PID)在SVC动态调节过程中由于控制器参数固定而存在动态响应、自适应能力差的问题,提出了一种基于小波神经网络PID(WNNPID)的SVC电流反馈电压稳定控制方法。首先,分别选取SVC的电压差ΔUr、电压误差ΔU和补偿电压USL作为WNNPID控制器的输入信号,而控制器的输出为SVC的参考电纳。然后,采用小波神经网络(WNN)和增量式PID控制对WNNPID控制器的结构进行设计。最后,采用Matlab/Simulink仿真平台对所提控制方法进行仿真,并与基于反向传播(BP)神经网络PID的控制效果进行了对比。仿真结果表明,所提WNNPID控制方法具有更稳定的电压控制效果、较快的响应速度、较好的动静态响应性能和较强的自适应能力。

基于PID控制的自适应密钥生成

在无线安全通信中,生成密钥并保证其稳定是至关重要的。目前绝大多数密钥生成方案都是以恒定的速率探测信道,没有考虑实际环境中由于信道状态的改变而导致信道探测效率和KGR(Key Generation Rate)不稳定的问题。为了使合法通信端的信道探测效率和KGR保持稳定,提出一种基于PID(Proportional Integral Derivative)控制的自适应密钥生成方案。该方案将实际信道探测效率与目标信道探测效率作比较,自适应引入AR(Artificial Randomness),从而保持信道探测效率的稳定;根据反馈的KGR与目标KGR比较,自适应调整信道探测速率,从而保持KGR稳定。仿真结果表明,不论信道环境变化得快与慢,自适应控制器都能将信道探测效率和KGR稳定在相应的目标值附近。

基于PID的陆区航路纵向碰撞风险控制

为提高陆区航路运行的安全性,减少间隔冲突隐患,仿真研究陆区航路纵向碰撞风险,首先,基于位置误差理论建立陆区航路的纵向碰撞风险数学模型,综合考虑飞行员干预时间,使用安全目标水平反推,确定航空器运行安全区域的前后边界;然后,使用Matlab软件的System Identification工具箱拟合辨识快速存取记录器(QAR)数据,得到反映发动机推力手柄角度和速度变化对应关系的传递函数,依此设计比例、积分与微分(PID)控制系统,将航空器纵向间隔控制在安全区域内;最后,使用Simulink搭建控制系统进行仿真,以验证控制系统功能。结果表明:在给定航路流量(50架/h)条件下,该控制系统能够实时计算2机纵向安全间隔范围(3326~16674 m),并在产生间隔冲突时于70 s内将前后机间隔调整至标称值。PID控制系统能够将碰撞风险始终维持在安全目标水平之上,提高航空器运行的安全性。

基于非线性PID的微电网虚拟同步发电机控制

为解决传统虚拟同步发电机(VSG:Virtual Synchronous Generator)电压电流双闭环控制的抗扰动性能差,受系统参数变化及各种不确定影响较大等问题,在建立微电网VSG转动惯量自适应控制模型的基础上,采用一种基于跟踪微分器的非线性PID(Proportion Integration Differentiation)实现电压外环和电流内环的控制,并根据输出信号的反馈值实时调节系统动态响应,从而达到稳定输出的效果。通过仿真验证了基于非线性PID的微电网虚拟同步发电机双闭环控制的正确性和有效性。

模糊PID控制下的CSPRs配对进近纵向碰撞风险

为控制近距离平行跑道(CSPRs)配对进近的纵向碰撞风险,提高该程序的运行安全,首先,基于快速存取记录器(QAR)数据分析航空器在进近过程中的速度变化特征,拟合其在进近阶段的定位误差分布;其次,考虑前机的尾流影响,建立基于位置误差的配对进近纵向碰撞风险模型,计算得到前后机的最优纵向间隔;然后,使用Matlab的System Identification工具箱,拟合辨识发动机推力手柄角度和飞机速度变化的相关数据,得到其对应关系的传递函数,根据该函数设计模糊比例、积分、微分(PID)控制系统,通过控制前后机间的纵向间隔使碰撞风险维持在最低值;最后,使用Simulink仿真验证该控制系统。结果表明:模糊PID控制系统在响应时间、超调量、稳定性等方面的表现皆优于传统的PID控制系统,该模糊PID系统能在50 s内将前后机的纵向碰撞风险控制在最低值(1.72×10-38次事故/飞行小时),并且在整个配对程序的实施过程中维持该值。

基于ESO和分数阶PID的改进P&O控制策略

为了提高光伏电池转换效率、降低能量损失,有必要研究最大功率点跟踪(maximum power point tracking,MPPT)方法。针对传统扰动观察法(perturbation observation method,P&O)存在无法兼顾跟踪速度与稳态精度、在光照度发生较大变化时会产生误判现象的问题,文中提出一种能适应环境变化的变步长P&O控制策略。首先,利用光伏电池刚启动时类似恒流源的特性获取当前光照度下的短路电流,通过固定电流法推导出最大功率点(maximum power point,MPP)的参考电压;其次,当光照度突变时,提出功率修正方法,并给出突变时的变步长调整策略;最后,设计基于线性扩张状态观测器(linear extended state observer,LESO)的分数阶比例积分微分(fractional order proportion integration differentiation,FOPID)控制器,可以对算法输出的参考电压进一步进行跟踪补偿。仿真结果表明,所提控制策略可以提高稳态精度和跟踪速度,有效提高光伏电池的输出功率。

Simulation on Driving System Used for Differential Steering of Electric Scooter

To investigate the feasibility and effectiveness of the designed control system used for driving and steering of an electric scooter,a model of differential steering was developed.The function of electronic differential steering was realized by controlling the speed of right or left wheel and the corresponding speed difference.The control system was simulated with MATLAB/SIMULINK and ADAMS.It is found that the motor load torque is proportional to the tire vertical force,so the adhesive capacity is met.The electric scooter can operate stably on the slope road at a speed of more than 1.5m/s and turn stably at yawing velocities of 10°and 90°per second.