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.

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BP神经网络的Fuzzy-PID恒温控制器

在高精度恒温系统温度控制中,为了克服温度这种被控对象因其纯滞后、非线性和大惯性特性以及被控对象参数的时变性对精度的影响,采用了BP神经模糊PID控制器代替模糊控制,使推理速度加快,并通过在系统运行时不断地增加和完善模糊控制规则,不断提高系统控制的精度。仿真分析结果表明,经过BP神经网络优化学习后,系统具有良好的控制性能和自适应能力,很好地满足了大滞后系统对高精度控温与快速性的要求。

Fuzzy PID control system for hose pulse experiment based on LabVIEW

The hose pulse testing bench generally uses electro-hydraulic servo system. It occupies little space, tracks signals fast and has simple structure, and therefore it is widely used in industrial control field. However, there are lots of problems such as little accuracy and instability caused by slow response of hydraulic and various interference factors. Simple proportional integra- tion derivatiation （PID） control method of traditional pulse bench is simple in principle, but it is difficult in parameter adjust- ment. According to the special requirements of the control system, a PID method based on fuzzy control is proposed in the pa- per. This method not only retains the advantages of the conventional control system, but also ameliorates the drawbacks of parameter uncertainty, instability and lag. It has been testified that the method is practicable and can improve the precision and adaptation.

PID-Based Bit Allocation Strategy for Asymmetric Stereoscopic Video Coding with Fuzzy Quality Controller

Asymmetric stereoscopic video coding can take advantage of binocular suppression in human vision by representing one of the two views in lower quality.This paper proposes a bit allocation strategy for asymmetric stereoscopic video coding.In order to improve the accuracy of bit allocation and rate control in the left view,a proportionalintegral-derivative controller is adopted.Meanwhile,to control the quality fluctuation between consecutive frames of the left view,a quality controller is adopted.Besides,a fuzzy controller is proposed to control the variation in quality between the left and right views by comparing the PSNR disparity of two views with a fixed threshold,which is used to quantize the binocular psycho-visual redundancy and adjust the quantization parameter (QP) of the right view correspondingly.The proposed algorithm has been implemented in H.264/AVC video codec,and the experimental results show its effectiveness in rate control while keeping a good quality for the left view,and fewer bits are allocated for the right view so that the overall bit rate is saved by 7.2% at most without the loss of subjective visual quality for stereoscopic video.

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.

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。

基于遗传算法整定驱动参数的水力发电机PID控制

为了弥补PID控制容易出现超调的现场,设计了一种基于遗传算法整定驱动参数的水力发电机PID控制方法。从正弦信号方面对水力发电机开展进给测试,促进了跟随性能的显著提升。研究结果表明:遗传算法整定方案在迭代到37次时达到收敛,幅频特性表明系统获得了理想响应性能。采用遗传算法整定驱动参数后获得更低跟随误差,表现出了更优的跟随性能。正弦信号下遗传算法参数整定时达到了更低跟随误差,表现出了更优的跟随性能。绝对误差极值降低35.5%,绝对误差积分降低50.1%。该研究有助于提高水力发电机控制稳定性,也可适用于其它的机械传动机构。

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。

模糊自整定PID温度控制系统的建模与仿真

针对炒茶机的加热控制系统跟踪设定的温度值滞后、自动调节加热装置实时性差的问题,设计一种模糊自整定比例积分微分(PID)参数控制器。采用PID控制和模糊控制算法相结合的方法,实现模糊控制对PID参数的调整。利用Matlab在Simulink中建立模型,并对该控制器进行仿真分析。结果表明,模糊PID自整定控制器的超调量≈1%,稳态误差es=0。该方法可提高温度控制系统的性能。

果园管道喷雾系统药液压力的自整定模糊PID控制

果园管道喷药系统具有非线性、大时滞特性,且管道中药液的压力随喷嘴数目变化而波动。为此,对管道中药液压力采用了带变速积分、微分先行优化算子的自整定模糊PID控制。根据管道中压力的实际值与设定值间的误差及误差变化趋势,在线调整模糊PID的参数,经带有变速积分、微分先行优化算子的增量式PID算法计算,获得控制量以控制管道中药液的压力。试验结果表明:采用这种控制方法,与不使用自整定模糊PID参数的变速积分、微分先行PID控制相比,管道中压力响应的上升时间缩短18.42%,调整时间缩短12.56%,最大超调量减小4.43%,误差减小50%。该控制方法能满足果园管道喷雾系统中对压力控制的要求。

孤岛运行微电网中模糊PID下垂控制器设计

针对微电网孤岛运行时分布式电源(DG)所采用的传统电压/频率下垂控制方法的不足,提出了一种具有比例—积分—微分(PID)结构的改进下垂控制方法,并根据电压和频率的变化,使用模糊推理技术来优化相应参数;设计了一种自适应模糊PID下垂控制器,以进一步有效减小微电网孤岛运行时由于扰动所造成的电压/频率振荡。通过仿真计算验证了所提出的模糊PID下垂控制器设计的正确性和有效性。

基于模糊PID的CVT夹紧力控制

针对金属带式无级变速器(CVT)夹紧力不合理造成传动效率低的问题,提出基于模糊PID的CVT夹紧力控制方法.为保证主、从动带轮夹紧力满足转矩传递要求同时实现夹紧力控制、速比控制解耦,研究了夹紧力的唯一控制原理.提出目标夹紧力的模糊计算方法,使目标夹紧力控制在最佳范围内.设计模糊PID控制方法,首先应用模糊控制使夹紧力快速稳定在目标夹紧力小范围内,然后通过PID寻优达到目标夹紧力.试验结果表明通过对目标夹紧力的模糊计算可以在保证不打滑的情况下,有效降低目标夹紧力;通过从动轮夹紧力的模糊PID控制方法,可以使从动轮夹紧力快速、准确达到目标夹紧力,并消除比例溢流阀饱和特性对控制性能的影响.

基于新型模糊PID算法的恒温控制系统研究

为解决传统恒温控制在工农业应用中自动化程度低、精准度不高以及稳定性差等问题,设计了一种基于新型模糊PID(Proportion Integration Differentiation)控制理论的自动控制系统。该系统应用STM32芯片对温度量进行采集和实时控制,使用矩阵键盘和液晶屏作为人机交互设备,引入新型模糊PID控制算法,构建新型模糊PID控制器,通过迭代算法整定比例、积分和微分参数值,得到加热控制量,控制器根据该控制量按对应的PWM(Pulse Width Modulation)高电平占空比输出电流量对半导体制冷片进行加热。实验表明,该系统能快速采集系统温度并进行实时控制,将系统温度稳定在预期温度,系统理论绝对误差为0.1℃,实际测量误差为0.5℃。系统较好地实现了温度自动检测与控制,解决了自动化程度低、精准度不高以及稳定性差的问题。

分数阶PID控制器参数的自适应设计

分数阶比例-积分-微分(proportional-integral-derivative,PID)控制器是整数阶PID控制器在复数域的推广,通过引入两个可调参数λ和μ使得控制器参数整定范围变大,但使得参数设计变得更加复杂。针对分数阶PID控制器的特点,提出了一种参数自适应的改进型差分进化算法整定方法,在目标函数的选取上,针对传统时间与绝对误差乘积准则对超调量控制不足,引入误差与控制信号加权,分别对整数阶和分数阶被控模型进行理论分析和数值仿真,仿真结果证明了该控制器参数设计方法的有效性和准确性。

基于自适应模糊PID的注塑机温度控制及仿真

为了对注塑机温度精确控制,针对注塑机温度在传统比例积分微分(PID)控制系统中存在的控制偏差大、调节速度慢等诸多不足的现状,提出将模糊控制器和PID控制器相结合,构造成一个自适应模糊PID控制器,运用MATLAB软件建立传统PID和自适应模糊PID控制器模型,对机筒温度进行仿真对比。设定200℃为给定温度,最终仿真结果显示,与传统PID控制器相比,自适应模糊PID控制器实现了注塑机温度的实时控制,达到了超调量小,升温快速及稳态无误差等要求。表明了自适应模糊PID控制器比传统PID控制器对温度的控制效果更加良好。

座舱功能试验压力控制系统建模与PID控制器设计

针对座舱功能试验压力控制系统参数调试不允许在飞机座舱上直接进行的问题,建立了包括气动减压阀阀芯构件动态特性与试验管道压力变化过程等因素在内的座舱功能试验压力控制系统模型,可使压力控制系统设计客观化、解析化.通过对系统模型进行线性化和拟合降阶,再根据降阶模型应用解析化方法,设计了座舱功能试验压力控制系统的比例、积分、微分(PID)控制器,并对设计结果进行了仿真验证.结果表明,解析化方法设计的PID控制器可以满足飞机座舱功能试验压力控制系统的技术要求.