Fuzzy self-tuning PID control of the operation temperatures in a two-staged membrane separation process

A two-staged membrane separation process for hydrogen recovery from refinery gases is introduced. The principle of the gas membrane separation process and the influence of the operation temperatures are analyzed. As the conventional PID controller is difficult to make the operation temperatures steady, a fuzzy self-tuning PID control algorithm is proposed. The application shows that the algorithm is effective, the operation temperatures of both stages can be controlled steadily, and the operation flexibility and adaptability of the hydrogen recovery unit are enhanced with safety. This study lays a foundation to optimize the control of the membrane separation process and thus ensure the membrane performance.

Smith Prediction Monitor AGC System Based on Fuzzy Self-Tuning PID Control

In accordance with the feature of pure delay in monitor AGC system for cold rolling mill, a new fuzzy selftuning PID Smith prediction controller is developed. The position control model is deduced based on a single stand cold rolling mill, and the fuzzy controller for monitor AGC system is designed. The analysis of dynamic performance for traditional PID Smith prediction controller and fuzzy self-tuning PID Smith prediction controller is done by MAT- LAB toolbox. The simulation results show that fuzzy self-tuning PID Smith controller has stronger robustness, faster response and higher static accuracy than traditional PID Smith controller.

Unified Control Theory from PID to ACPID

To address the challenge of achieving unified control across diverse nonlinear systems, a comprehensive control theory spanning from PID (Proportional-Integral-Derivative) to ACPID (Auto-Coupling PID) has been proposed. The primary concept is to unify all intricate factors, including internal dynamics and external bounded disturbance, into a single total disturbance. This enables the mapping of various nonlinear systems onto a linear disturbance system. Based on the theory of PID control and the characteristic equation of a critically damping system, Zeng’s stabilization rules (ZSR) and an ACPID control force based on a single speed factor have been designed. ACPID control theory is both simple and practical, with significant scientific significance and application value in the field of control engineering.

System Identification and Parameter Self-Tuning Controller on Deep-Sea Mining Vehicle

System identification is a quintessential measure for real-time analysis on kinematic characteristics for deep-sea mining vehicle, and thus to enhance the control performance and testing efficiency. In this study, the system identification algorithm, recursive least square method with instrumental variables(IV-RLS), is tailored to model ‘Pioneer I’, a deep-sea mining vehicle which recently completed a 1305-meter-deep sea trial in the Xisha area of the South China Sea in August, 2021. The algorithm operates on the sensor data collected from the trial to obtain the vehicle’s kinematic model and accordingly design the parameter self-tuning controller. The performances demonstrate the accuracy of the model, and prove its generalization capability. With this model, the optimal controller has been designed, the control parameters have been self-tuned, and the response time and robustness of the system have been optimized,which validates the high efficiency on digital modelling for precision control of deep-sea mining vehicles.

Fuzzy Self-Tuning PID Control of Hydrogen-Driven Pneumatic Artificial Muscle Actuator

In this paper, a fuzzy self-tuning Proportional-Integral-Derivative （PID） control of hydrogen-driven Pneumatic Artificial Muscle （PAM） actuator is presented. With a conventional PID control, non-linear thermodynamics of the hydrogen-driven PAM actuator still highly affects the mechanical actuations itself, causing deyiation of desired tasks. The fuzzy self-tuning PID con- troller is systematically developed so as to achieve dynamic performance targets of the hydrogen-driven PAM actuator. The fuzzy rules based on desired characteristics of closed-loop control are designed to finely tune the PID gains of the controller under different operating conditions. The empirical models and properties of the hydrogen-driven PAM actuator are used as a genuine representation of mechanical actuations. A mass-spring-damper system is applied to the hydrogen-driven PAM actuator as a typical mechanical load during actuations. The results of the implementation show that the viability of the proposed method in actuating the hydrogen-driven PAM under mechanical loads is close to desired oerformance.

A Design of a PID Self-Tuning Controller Using LabVIEW

In this paper a trial has been made to design a simple self-tuning LabVIEW-based PID controller. The controller uses an open-loop relay test, calculates the tuned parameters in an open loop mode of operation before it updates controller parameters and runs the process as a closed-loop system. The controller reacts on a persistent offset error value as a result of load disturbance or a set point change. Practical results show that such a controller may be recommended to control a variety of industrial processes. A GUI was developed to facilitate control-mode selection, the setting of controller parameters, and the display of control system variables. GUI makes it possible to put the controller in manual or self-tuning mode.

Development of an Enhanced Self-Tuning RBF-PID Controller for Achieving Higher Energy-Efficient Process Control

Proportional, integral and derivative (PID) control strategy has been widely applied in heating systems in decades. To improve the accuracy and the robustness of PID control, self-tuning radial-basis-function neural network PID (RBF-PID) is developed and used. Even though being popular, during the control process both of PID and RBF-PID control strategy are inadequate in achieving simultaneous high energy-efficiency and good control accuracy. To address this problem, in this paper we develop and report an enhanced self-tuning radial-basis-function neural network PID (e-RBF-PID) controller. To identify the superiority of e-RBF-PID, following works are conducted and reported in this paper. Firstly, four controllers, i.e., on-off, PID, RBF-PID and e-RBF-PID are designed. Secondly, in order to test the performance of the e-RBF-PID controller, an experimental water heating system is constructed for being controlled. Finally, the energy consumption for the four controllers under the three control scenarios is investigated through experiments. The experimental results indicate that in the three scenarios, the developed e-RBF-PID controller outperforms on-off controller as having higher accuracy. Compared to the PID controller, the e-RBF-PID controller has higher speed in control, and the experimental results show that settling time savings is between 12.6% - 49.0%. Most importantly, less control energy consumption is obtained if using the e-RBF-PID controller. It is found that up to 28.5% energy consumption can be saved. Therefore, it is concluded that the proposed e-RBF-PID is capable of enhancing energy efficiency during control process.

Design and simulation about a self-Tuning fuzzy-PID controller

Fuzzy logic has attracted the attention of structural control engineers during the last few years,because fuzzy logic can handle nonlinearities,uncertainties,and heuristic knowledge effectively and easily.In this paper,a self-Tuning fuzzy-PID control method which used the technology of the fuzzy control and PID control unified is presented.These techniques can visualize the results and processes for structure stress.These techniques will also provide convenience for engineers and users,and have high practical values.The MATLAB simulation result shows that the system precision and the efficiency are very high and the static error is small,and robustness was also validated.

采摘机器人机械手避障方法研究——基于遗传优化和模糊PID控制器

为了提高采摘机器人机械手移动的自动化程度,使其在移动过程中有效避开障碍物,将基于PID控制器的避障系统引入到了机械手的设计上,并采用遗传算法和模糊算法对控制器进行了优化,提高了机械手移动的准确性和平稳性。模拟采摘机器人机械手的作业环境,对机械手的避障功能进行了测试,结果表明:机械手可以成功躲避障碍物,且采用遗传和模糊算法优化的机械手具有更高的移动平稳性,对于提高果实采摘效率和质量具有重要的作用。

基于PID的ROV运动控制仿真

针对观察型水下机器人在水下运动时易受暗流、波浪影响,造成操控困难、系统稳定性差等问题,建立遥控水下机器人(Remotely Operated Vehicle,ROV)不同运动的控制模型,考虑电机和导管螺旋桨推进器的传递函数对ROV控制系统的影响,确定定艏向和定深控制系统的闭环传递函数,结合模糊控制和比例积分微分(Proportional Integral Differential,PID)控制法,得到模糊PID控制器,基于MATLAB/Simulink环境进行ROV定深度运动仿真和ROV水平面艏向定偏角运动仿真。结果表明,与传统PID控制相比,模糊PID控制具有更优的ROV定艏向和定深度控制效果,不会发生超调现象,在抗干扰能力和响应速度方面具有明显的优势,可有效地实现ROV定艏向和定深度运动控制。

基于遗传算法的汽车主动悬架变论域模糊PID控制

针对1/4车主动悬架系统,提出一种基于遗传算法(genetic algorithm,GA)的变论域模糊比例-积分-微分(proportional-integral-differential,PID)控制方法.在建立主动悬架系统模型的基础上,引入变论域思想设计模糊PID控制器.为进一步改善控制器减振效果,采用GA来优化变论域中伸缩因子描述函数的参数.结果表明:相比PID、模糊PID与未优化的变论域模糊PID等控制方法,基于GA的变论域模糊PID控制方法在降低车身垂向加速度、改善乘坐舒适性方面具有优越性;所提控制方法对簧载质量和车辆行驶速度不确定性具备较强的鲁棒性.

基于模糊-遗传算法的粗纱机出料传动系统PID参数优化方法

目前常规的粗纱机出料传动系统PID参数优化方法主要通过结合被控对象的稳态增益情况构建出优化函数,实现PID参数优化整合,忽略了实际运行工况下的干扰成分,导致参数优化效果不佳。对此,提出基于模糊-遗传算法的粗纱机出料传动系统PID参数优化方法。首先结合PID的系统阶跃响应曲线,以被控量的偏差变化率作为主要参数选取性能指标;然后通过引入负荷扰动模块以及频率干扰模块,对PID调节模块进行建模分析;最后结合模糊-遗传算法对最优适应度值进行求解,从而实现PID参数的有效优化,并对提出的方法进行优化效果检验。最终测试结果表明,采用提出的方法对PID参数进行优化整合时,控制输出曲线与理论输出曲线的拟合程度更高,具备较为理想的优化效果。

基于BPNN-PID控制策略的果蔬保鲜环境参数调控优化

【目的】开发新的控制策略,用于解决传统控制方法果蔬保鲜环境参数因时变性、非线性、滞后性强和惯性大等特点导致的控制精度低、鲁棒性弱等问题。【方法】将传统比例−积分−微分(Proportional-integral-derivative,PID)和BP神经网络(Back-propagation neural network,BPNN)算法相结合,开发一种基于BPNN-PID的控制策略,通过自主搭建的果蔬保鲜环境调控试验平台和自主设计的控制系统,研究不同控制策略对保鲜环境参数调控效果的影响。【结果】基于BPNN-PID控制策略的果蔬保鲜环境控制系统,环境温度超调量为1.7℃、稳定时间为80 min、稳态误差为±0.2℃,环境相对湿度超调量为2.8%、稳定时间为55 min,相对湿度稳定维持在80%~90%范围内。与传统PID控制策略相比,BPNN-PID控制策略环境温度超调量减小了2.1℃、稳态误差减小了0.3℃、稳定时间缩短了25 min,环境相对湿度超调量减小了2.2%、稳定时间缩短了25 min,环境参数波动幅度均有所降低。【结论】本文开发的果蔬保鲜环境控制系统呈现出良好的动态调整能力,具有较强的鲁棒性,控制性能明显提升,实现了保鲜环境参数的精准控制,满足果蔬保鲜贮藏要求。研究结果为果蔬保鲜环境参数调控提供了参考。

基于粒子群优化模糊PID控制的水厂加氯系统

水厂加氯过程具有非线性、大时滞等特点,采用传统的PID控制方式难以实现消毒剂精准投加。因此,以某采用次氯酸钠消毒系统的水厂为例,首先通过分析该水厂加氯系统工作原理,结合工程经验和运行数据确定了加氯过程的近似数学模型。然后综合使用粒子群优化算法、模糊控制算法和PID控制算法,设计了一种粒子群优化模糊PID控制器,并利用MATLAB软件搭建了粒子群优化模糊PID控制系统和传统PID控制系统的模型进行仿真验证,结果表明:相较于传统PID控制系统,粒子群优化模糊PID控制系统的超调量减少了89.36%,调节时间减少了46.63%,其抗干扰能力也更强,系统整体控制效果有了较大提升。最后使用基于粒子群优化的模糊PID控制法对水厂加氯控制系统进行改造,试运行后发现,新系统控制效果良好,出厂水游离氯值能够稳定在设定值附近。

基于单片机和PID算法的温度智能控制系统设计

为提高温度控制的智能化水平,设计一种智能温度控制系统。该系统以STC89C52单片机为控制器,采用PID算法控制温度,具有语音播报和手机远程控制等功能。采用DS18B20温度传感器采集环境温度;设计LCD12864显示电路实时显示当前温度、温度上下限以及温度状态;设计WT588D语音提醒电路,当测量温度小于下限或大于上限时发出语音提醒;设计按键电路实现温度上下限值的设定;设计蓝牙通信电路,与手机APP通信,实现远程控制;采用PID算法输出控制量,控制固态继电器驱动加热或降温装置,实现温度控制。其次,对温度控制系统的硬件和软件进行设计,并制作实物进行运行测试。实验结果表明,所设计的温度控制系统能够很好地实现温度控制,从而达到预期效果,且操作方便、成本低,具有较强的应用价值。

带宽化调节下的抗扰PID退饱和控制方法

比例–积分–微分(PID)控制的抗饱和设计是一类经典非线性控制问题,理论研究丰富,但工程中广泛使用的仍是积分退饱和这类简易策略.究其原因,现有理论方法过于依赖系统模型和状态空间理论,稳定性条件与调参机制之间关系复杂.本文基于经典退饱和控制结构,首次阐述了“退饱和带宽”概念及退饱和参数调节机理,在此基础上讨论了两类低阶退饱和补偿器的带宽化设计方法.在抗扰PID反馈控制下,结合小增益定理得到退饱和控制的稳定性条件,给出控制参数与退饱和带宽之间的调节关系,实现退饱和控制的带宽化整定.最后,通过数值仿真验证了所提方法的有效性.

重放攻击下多智能体系统H_(∞)一致性PID控制

本文针对一类带有加性噪声和乘性噪声的离散多智能体系统,研究重放攻击下多智能体系统的H_(∞)一致性比例-积分-微分(PID)控制问题.首先,根据智能体的测量输出设计状态观测器,对智能体的状态进行有效估计,观测器设计过程中考虑了系统测量输出从传感器传输到观测器过程中受到重放网络攻击的影响.然后,利用智能体与其邻居智能体的估计状态差设计PID控制器.利用李雅普诺夫稳定性理论和代数图论,证明在该控制策略下,多智能体系统在重放攻击存在的情况下达到预期的H_(∞)性能指标.最后,利用线性矩阵不等式(LMI)方法求解观测器和控制器增益,利用数值仿真验证了所设计的观测器和PID控制器的有效性.

基于变论域模糊PID的金属热处理过程温度控制方法

为高效且安全地提取金属合金内的可用材质并分析其性能,利用变论域模糊PID技术,实现对热处理过程的温度控制。设定在金属规则状态不变的情况下,论域会随着偏差的缩减出现伸缩,即需要增添新的规则。若偏差率能够支持模糊量化取整,控制器的伸缩因子可能会陷入无法取值的情况。此时,通过二元函数分片双一次插值法计算伸缩因子,在临近分档之间填充新规则,确保伸缩因子取值的连续性。然后,在模糊PID控制过程中加入变论域,依靠控制器实现对金属热处理过程温度的控制。根据实验验证情况可知:该方法有效提高了控制效果,在应用该方法后,缩短了温度控制时间,在100s内就可以是加热温度到达拟定值。

多策略改进粒子群优化AGV模糊PID控制

为解决AGV在复杂环境下控制精度低下、响应速度慢和鲁棒性差等问题,提出一种基于改进粒子群优化模糊PID控制方法。在粒子群算法(PSO)引入Logistic混沌映射对种群进行初始化,其次对惯性权重和学习因子进行非线性控制更新,提升种群寻优能力和避免陷入局部最优;选取9个测试函数验证改进PSO算法效果。仿真结果表明改进PSO在寻优精度和收敛速度都优于其他3种算法,而且不易陷入局部最优。最后对被控系统进行效果验证,结果表明改进PSO优化模糊PID控制器在常规和外部干扰两种环境下控制性能都优于传统PID和模糊PID,具有高可靠性、高控制精度,满足系统要求。

基于PID的电阻炉温度控制仿真

电阻炉是熔炼金属、玻璃成型及陶瓷制造等工业生产中的重要加热设备之一,主要利用电流使炉内电热元件进行发热,在工业背景下,电阻炉温度的准确控制对于生产过程的稳定性和产品质量至关重要。如果温度控制不足够精确,可能会导致材料变质、能源浪费和生产延误,造成一定的经济损失。为了优化这些过程并确保产品质量,精确的温度控制是必不可少的。在分析PID控制器基本原理以及特点的基础上,探讨基于PID的电阻炉温度控制系统设计,并通过Matlab的强大工具和模拟环境,模拟电阻炉温度控制代码,能够帮助进一步理解和改进电阻炉温度控制系统,使电阻炉温度控制更加精准,以满足不断变化的工业需求,生产出更高质量的工业制品,推动制造业的现代化发展。