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.

Design of PID controller with incomplete derivation based on differential evolution algorithm

To determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution （DE） algorithm is presented. The controller is called DE-PID controller. To overcome the disadvantages of the integral performance criteria in the frequency domain such as IAE, ISE, and ITSE, a new performance criterion in the time domain is proposed. The optimization procedures employing the DE algorithm to search the optimal or near optimal PID controller parameters of a control system are demonstrated in detail. Three typical control systems are chosen to test and evaluate the adaptation and robustness of the proposed DE-PID controller. The simulation results show that the proposed approach has superior features of easy implementation, stable convergence characteristic, and good computational efficiency. Compared with the ZN, GA, and ASA, the proposed design method is indeed more efficient and robust in improving the step response of a control system.

Application of PID controller to 2D differential geometric guidance problem

This paper presents the application of the proportional-integral-derivative （PID） controller to the flight control system （FCS） for two-dimensional （2D） differential geometric （DG） guidance and control problem. In particular, the performance of the designed FCS is investigated. To this end, the commanded angle-of-attack is firstly developed in the time domain using the classical DG formulations. Then, the classical PID controller is introduced to develop a FCS so as to form the 2D DG guidance and control system, and the PID controller parameters are determined by the Ziegler-Nichols method as well as the Routh-Hurwitz stability algorithm to guarantee the convergence of the system error. The results demonstrate that the designed controller yields a fast responding system, and the resulting DG guidance and control system is viable and effective in a realistic missile defense engagement.

平面并联机器人离线PID控制优化研究

为了使平面并联机器人在闭环控制系统中具有更稳定的控制性能。对此,这里构建了平面五杆并联机器人动力学模型及其控制系统模型,给出了控制系统的增益矢量p。对机器人系统模型进行离线PID控制优化,通过控制增益来设计控制系统中的增益矢量p,从而实现非线性单目标动态优化(NLMODOP)。在NLMODOP中加入动态约束,采用带约束处理机制的差分进化(DE)算法求解平面并联机器人的非线性规划问题,进而处理不稳定的动态优化。对机器人模型中的五个连杆进行仿真实验,并对有无DE算法控制的仿真结果进行了比较。结果表明:相比于无DE算法,采用DE算法下的机器人系统模型的连杆跟踪位移基本无跟踪误差。说明基于差分进化算法的平面并联机器人离线PID控制优化具有较好的控制精度和跟踪性能。

爆胎车辆稳定性控制的模糊PID控制研究

为了使汽车在爆胎后能继续维持爆胎前的行驶状态,对其稳定性控制做了一些研究工作。首先,建立轮胎模型,汽车二自由度理想模型、七自由度实际模型。其次,将FPID运用在爆胎汽车的稳定性控制上,以两模型的横摆角速度偏差(E)及偏差变化率(EC)作为FPID控制器的输入,决策出用以维持爆胎汽车的稳定性的最优附加横摆力矩。最后对所提的FPID控制进行仿真验证。结果表明:FPID控制可以降低爆胎汽车横摆角速度的变化幅度,保证了高速行驶中爆胎车辆的稳定性。

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.

Simulation research on control algorithm of differential pressure casting process

To improve the precision of the filling pressure curve of differential pressure casting controlled with PID controller,the model of differential pressure casting process is established and two pressure-difference control systems using PID algorithm and Dahlin algorithm are separately designed in MATLAB. The scheduled pressure curves controlled with PID algorithm and Dahlin algorithm,respectively,are comparatively simulated in MATLAB. The simulated pressure curves obtained show that the control precision with Dahlin algorithm is higher than that with PID algorithm in the differential pressure casting process,and it was further verified by production practice.

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.

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.

基于改进跟踪微分器与前馈的模糊自适应PID控制

经典PID控制器在工程领域应用广泛,但其快速性和超调量之间不能很好地实现协调控制,且误差信号在经典意义上不可微,导致微分难以被有效利用。基于此,在典型跟踪微分器研究的基础上,采用一种具有良好的动态响应和滤波功能的改进非线性跟踪微分器对输入信号进行快速、无超调跟踪。并结合前馈控制和模糊自适应PID控制提升系统的稳定性和快速性,设计了一种基于改进跟踪微分器与前馈的模糊自适应PID控制器。以移相全桥变换器为控制对象,用经典PID与其进行对比分析。仿真与实验波形分析发现,所提控制器的鲁棒性更高,同时可将调节时间缩短至40%,响应速度也更快。

冷链运输车空调冷风机运行自适应PID控制方法研究

冷链运输车空调冷风机运行环境复杂,导致传统的比例积分微分(PID)控制过程难以准确设定控制参数。为解决这一问题,设计冷链运输车空调冷风机运行自适应PID控制方法。首先,通过分析冷链运输车空调冷风机工作过程,选取多个控制参数,以降低复杂度。然后,设计基于自适应PID控制器的控制模型,以送风温度和送风压力作为控制量,将冷、热水流量及风机转速作为被控制量,求解控制量和被控制量的传递函数。最后,根据传递函数与期望值间的耦合关系,对控制分量的增减进行调整,以实现冷链运输车空调冷风机运行的自适应PID控制。试验结果表明,该方法能够使冷链运输车内温度保持稳定。该方法控制精准度高、鲁棒性强、实用价值高,可以确保货物在整个冷链运输过程中的质量和安全。

基于PID算法的轧钢加热炉煤气智能燃烧控制研究

常规的轧钢加热炉煤气智能燃烧控制方法主要使用Fuzzy双交叉限幅控制器进行控制阶跃响应,易受温变超调作用的影响,导致燃烧效率偏低。基于此,提出一种基于比例-积分-微分(Proportion Integral Differential,PID)算法的轧钢加热炉煤气智能燃烧控制方法。生成轧钢加热炉煤气智能燃烧控制策略,利用PID算法设计轧钢加热炉煤气智能燃烧控制器,从而实现轧钢加热炉煤气智能燃烧控制。实验结果表明,设计的轧钢加热炉煤气智能燃烧PID算法控制方法在不同控制起始时间下的煤气智能燃烧效率均较高,控制性能良好,具有较高的实际应用价值。

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。

煤矿井下自动化钻机钻速模糊PID自适应控制方法

为提高井下作业质量,实现对钻机在工作中转速的精确、高效控制,以某煤矿工程为例,开展其井下作业过程自动化钻机钻速模糊比例-积分-微分(Proportion Integral Differential,PID)自适应控制方法的设计研究。根据钻机的动力系统,建立钻机动力函数,计算钻机推力,建立煤矿井下自动化钻机数学模型。将输入变量(转速误差、误差变化率)精确值转换为模糊集合的隶属度,设计基于模糊PID的钻机转速输入控制。在钻机上安装多种传感器,实时监测钻机的各项工作参数,利用模糊PID控制器,进行自动化钻机转速的自适应调节。对比实验结果表明:设计的方法可以实现对钻机转速的快速、准确控制,保证钻进速度的稳定性。

针对时滞积分系统的自适应动态PID控制器参数整定方法

【目的】PID控制器被广泛应用于积分系统的控制中,在积分系统的实际应用中,控制效果通常会受到延迟时间的影响,同时,被控积分参数变化较大的情况下,也会影响控制器的控制效果。【方法】提出了一种适用于时滞积分系统的自适应动态PID控制器参数整定方法,考虑了积分系统的电压、被控对象的变化范围、延迟时间等参数的影响,使得PID控制器的参数与积分系统控制对象之间的关系更加明确,同时考虑到实际应用中微分滤波器参数的整定。【结论】在Simulink中模拟了被控积分参数10倍变化的非线性系统,实现了对非线性系统的动态控制,达到了良好的控制效果,并在实际的PWM控制系统中验证了该方法应用的可行性。

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。

Computer Vision-Control-Based CNN-PID for Mobile Robot

With the development of artificial intelligence technology,various sectors of industry have developed.Among them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively conducted.Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed.In this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions.The robot should be able to follow the trajectory with speed control.Two controllers were used simultaneously to achieve this.Convolutional neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed control.This study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.

基于模糊PID的山地拖拉机调平控制系统的设计

在自行开发的山地拖拉机调平机构上加装了自动调平控制系统。系统以可编程逻辑控制器(PLC)为主控核心,以倾角传感器为车身平台倾角检测机构,以模糊PID为调平控制算法,通过监测倾角传感器检测的角度值来实时调整伺服电机的转动与伺服电缸的伸缩,以实现车体平台的自动调平。静态试验结果表明,拖拉机车体平台在倾斜15°的情况下,车体平台横向和纵向单独完成调平分别用时1.851 s和1.882 s,同时调平在3.319 s内完成。动态试验结果表明,拖拉机在行驶速度1.73 km/h、最大坡度15°时,完成车体平台横向和纵向调平分别用时6.253 s和6.853 s;调平时最大超调角分别为9.053 3°和8.687 2°,调平后车体平台角度偏差最终可控在0.5°。

基于反步自适应分数阶PID的四旋翼控制

为强化四旋翼无人机在受到自然风干扰情况下的抗干扰能力以及鲁棒性,提出一种将反步自适应控制与改进分数阶PID控制相结合的控制策略。首先分析四旋翼无人机的力学原理,建立受到气流干扰的四旋翼数学模型;然后通过不完全微分与前馈补偿改进传统PID控制器,用于控制四旋翼的外环位置子系统,设计反步自适应控制器用于控制内环姿态子系统,同时通过Lyapunov稳定性理论验证姿态子系统的稳定性;最后在Simulink平台进行仿真实验。实验结果表明,基于反步自适应控制与改进分数阶PID控制相结合的控制策略设计的控制器相较传统PID控制器、反步自适应控制器在快速性、稳定性、准确性方面更具优势。