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.

Application of Single Neuron Adaptive PID Regulators with Auto-tuning Gain in Industrial Parameter(Pressure)Closed-loop Process Control Systems

A type of single neuron adaptive PID regulator with auto-tuning gain is proposed and applied to the work control of fans, waterpumps and air-pressers etc. in Handan Iron & Steel Compel China. The robusthess of induStrial parameter closed-loop process controlsystems is improved, and the work quality of the systems bettered.

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.

Neural Model-Based Self-Tuning PID Strategy Applied to PEMFC

This paper illustrates the benefits of a self-tuning PID strategy applied to a proton exchange membrane fuel cell system. Controller parameters are updated on-line, at each sampling time, based on an instantaneous linearization of an artificial neural network model of the process and a General Minimum Variance control law. The self-tuning PID scheme allows managing nonlinear behaviors of the system while avoiding heavy computations. The applicability, efficiency and robustness of the proposed control strategy are experimentally confirmed using varying control scenarios. In this aim, the original built-in controller is overridden and the self-tuning PID controller is implemented externally and executed on-line. Experimental results show good performance in setpoint tracking accuracy and robustness against plant/model mismatch. The proposed strategy appears to be a promising alternative to heavy computation nonlinear control strategies and not optimal linear control strategies.

Design for Two-degree-of-freedom PID Regulator Based on Improved Generalized Extremal Optimization Algorithm

A kind of new design method for two-degree-of-freedom(2DOF)PID regulator was presented,in which,a new global search heuristic--improved generalized extremal optimization(GEO)algorithm is applied to the parameter optimization design of 2DOF PID regulator.The simulated results show that very good dynamic response performance of both command tracking and disturbance rejection characteristics can be achieved simultaneously.At the same time,the comparisons of simulation results with the improved GA,the basic GEO and the improved GEO were given.From the comparisons,it is shown that the improved GEO algorithm is competitive in performance with the GA and basic GEO and is an attractive tool to be used in the design of two-degree-of-freedom PID regulator.

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.

船用制淡制冷一体机真空泵PSO-PID的智能调控

船用制淡制冷一体机系统性能优化的关键在于真空泵的3个比例-积分-微分(proportional-integral-derivative,PID)调控参数K_(p)、K_(i)和K_(d),它们直接影响制冷温度和蒸发速率(制冷量和淡水产率)。针对真空泵PID参数难整定的问题,应用MATLAB Simulink,并结合船用制淡制冷一体机真空泵控制系统传递函数,将粒子群优化算法(particle swarm optimization,PSO)运用到PID算法模型优化中进行PID参数优化。结果表明:与基于临界比例度法的PID控制器参数整定相比,PSO-PID优化算法的调节时间缩短2.8%,超调量降低4.5%,说明PSO算法优化后的船用制淡制冷一体机PID控制具有更快的响应速度和更好的鲁棒性,有效提高了控制质量,实现船用制淡制冷一体机真空泵PSO-PID的智能调控。

Robust Non-fragile PID Controller Design for the Stroke Regulation of Metering Pumps

This job focuses on the stroke regulation of a class of high-precision metering pumps.A parametertuning method of robust non-fragile PID（proportional-integral-derivative）controllers is proposed with the assumption that a PID controller has additive gain perturbations.An H-infinite robust PID controller can be obtained by solving a linear matrix inequality.This approach can guarantee that the closed-loop control systems is asymptotically stable and the H-infinite norm of the transfer function from the disturbance to the output of a controlled system is less than a given constant to attenuate disturbances.The simulation case shows that the control performance of the proposed strategy is significantly better than the traditional PID approach in the situation with perturbations of controller parameters.

Research on DSP-Based Automatic Excitation Regulator in Small Rural Hydropower Station

Excitation regulator has played an important role to ensure that generator run safely and stably. However, in some remote areas, the rural small generator excitation control system is very backward, and it has hindered the development of Chinese agriculture and the pace of new countryside construction. This paper introduced a kind of structure and basic principle of DSP-based automatic excitation regulator, which was applied to rural small generator. We chose TMS320LF2407A chip as the core of regulator control system; the regulator adjusted PWM based on PID control strategy to control the exciting current. The theoretical analysis showed that the regulator had the characteristics of simplicity and reliability, well dynamic and static characteristics, and it could also adjust its own state quickly. The result was applicable to small rural hydropower station.

基于继电反馈PID自整定的加热系统

某工厂对某种液体进行加热控制,针对某液体复杂的反应过程,设计出带自整定功能的PID自动调节加热设备。该设计以意法半导体公司(ST)的STM32F103C8T6为控制器,以PT100为温度采集元件,控制可控硅进行过零功率调节,通过MCGS触摸屏来显示,设定有关波形和参数,在控制过程中,可以根据有关加热要求选择多种整定参数,以满足液体的不同动态响应类型。该文主要介绍系统硬件组成原理框图,给出系统主要硬件电路图和自整定算法的工作流程,并对PID程序编制的一些关键点进行说明。实验证明加热系统能满足工厂要求。

基于改进粒子群优化算法的PID控制器参数整定

针对标准粒子群优化算法优化PID控制器参数存在的早熟以及陷入局部最优等问题,引入粒子二阶振荡环节、反向学习、自适应惯性权重,并结合设计的惯性权重动态调整策略、粒子越界随机反射墙,提出一种新型分阶段迭代的改进粒子群优化算法。在6个标准测试函数上进行测试,其性能相比于一些已有的改进粒子群优化算法更加优秀。将这些算法用于自动电压调节器系统的PID控制器参数整定,仿真结果表明,所提出的改进粒子群优化算法整定的PID控制器的控制效果更好。

基于模糊神经网络的氢液化氦气压力PID控制

为了解决氢液化装置氦气压力调节系统超调量大、响应速度慢、调节时间长、控制参数无法在线整定等问题,针对系统具有非线性和时变性的特点,设计了基于模糊神经网络的PID控制器以及基于双曲正切函数的改进型激活函数。仿真结果表明:相比传统PID控制或模糊PID控制,采用模糊神经网络PID控制的系统动态性能显著改善,使得氢液化装置的氦气压力调节更加稳定可靠。

基于改进海马优化算法的PID参数优化

为了解决传统海马算法(SHO)在PID参数整定中存在全局寻优能力差且收敛速度慢的问题,提高PID参数优化质量,提出一种改进的海马优化算法(ISHO)。通过Tent混沌映射增加海马种群初始化多样性提高收敛速度;引入逃逸能量调控策略改进算法全局搜索与局部开发的转换机制,从而提高算法的全局寻优能力。将改进海马优化算法与传统海马算法、Z-N临界比例法、灰狼优化算法和粒子群优化算法进行比较,仿真结果表明:改进的海马优化算法优化PID参数具有调整时间更短、系统控制精度更高和收敛速度更快等优点,为PID控制系统的参数优化提供了参考。

基于模糊自整定PID算法的退火炉温度调节技术

由于未考虑到退火炉温度偏差值的影响,导致超调量较高,调节效果欠佳。为此,提出基于改进模糊自整定PID算法的真空退火炉温度动态调节技术。依据真空退火炉的工作机理,分析退火炉在工作中的温度变化曲线,采用双曲正切函数求取温度调节过程中的温度偏差值,从而对其动态温度进行设定,采用改进模糊自整定PID算法构造温度模糊控制器,通过对设计的温度模糊控制器内部控制参数进行优化,从而对控制器结构进行更新,并将退火炉的相关参数作为输入量,目标温度作为输出量,由此实现对退火炉温度的动态调节。实例应用结果显示,所提方法可以有效实现对真空退火炉温度的动态调节,且超调量较低,调节效果较好。

抽水蓄能机组空载工况动态矩阵PID串级控制

针对传统PID控制对含机械时滞抽水蓄能机组调节系统控制效果不理想的问题,提出了一种抽水蓄能机组的动态矩阵PID串级控制策略。考虑液压执行机构的机械时滞和主接力器的限幅与限速,建立了小波动情况下抽水蓄能机组调节系统的数学模型。在调节系统原PID控制作为内环控制的基础上,引入动态矩阵控制作为外环控制,通过天牛须搜索算法来整定优化PID参数,提出了一种新型抽水蓄能机组动态矩阵PID串级控制方法,并应用PID等6种控制策略对调节系统进行控制仿真。结果表明:所提出的控制方法可以在不同机械时滞作用下有效改善抽水蓄能机组调节系统在空载频率扰动时的控制性能。研究成果可为抽水蓄能机组的调节系统控制提供借鉴。

光伏发电大量并网下电力负荷调节模糊PID自动化控制研究

为平衡光伏发电大量并网后电网各节点的负荷偏差,该文优化设计光伏发电大量并网下电力负荷调节模糊PID自动化控制方法。模拟光伏发电大量并网过程,根据电力负荷数据的变化规律,预测并网环境中的电力负荷与调节控制量,综合考虑电网线路状态、线路容量等因素,选择电力负荷调节线路,生成电力负荷调节模糊PID自动化控制指令,在模糊PID自动化控制器作用下,实现电力负荷调节控制任务。

基于改进PID-LQR的导弹稳定控制方法研究

导弹稳定控制系统是提高导弹制导性能的关键技术之一,一直以来都是研究的热点和重点。以导弹纵向平面为例,建立了导弹稳定控制的状态空间模型,推导了LQR控制算法,在传统LQR方法的基础上,结合PID算法提出了一种改进的PID-LQR导弹稳定控制方法。通过仿真分析,比较了所提方法和传统LQR方法、改进LQR方法、模糊PID-LQR方法的控制性能,并对建立的导弹模型参数进行了拉偏试验,仿真结果表明所提出的方法优于其他3种方法,具有良好的控制性能和鲁棒性。

基于改进RBF/模糊PID的锻压机双缸闭环同步信号控制

为了提高连杆驱动锻压机双缸同步控制精度,设计了一种推进过程改进RBF/模糊PID控制方案,采用闭环同步控制。构建了改进RBF/模糊PID控制器,并通过Simulink完成仿真测试,验证了推进过程的同步控制性能。研究结果表明:无论是高速还是低速,改进RBF/模糊PID相对模糊PID控制方式表现出来更低的双缸同步误差,可以获得更高的系统控制精度。

模糊PID结合PSS的复合励磁系统研究

为了提高同步发电机在遭遇扰动时机端电压的稳定性,同时抑制其转速、电功率和功角的低频振荡,给出了一种模糊PID(FPID)结合电力系统稳定器(PSS)的复合励磁系统,并对其设计流程进行了阐述。运用MATLAB/Simulink搭建基于单机-无穷大系统的FPID+PSS励磁控制系统仿真模型,仿真结果表明FPID+PSS励磁控制系统能够有效地提高发电机机端电压的稳定性,且对其转速、电功率和功角的低频振荡有良好的抑制作用。

基于模糊自适应PID的煤矿提升机变频调速控制方法

煤矿提升机通常需大功率电动机提供动力,能源消耗巨大,导致煤矿提升机控制波特率较小,因此,设计一种基于模糊自适应PID的煤矿提升机变频调速控制方法。根据煤矿提升机的实际工况和性能要求,选择适当的变频调速范围。运用模糊自适应PID方法,根据煤矿提升机的实时状态和控制目标,确定变频调速的控制参数。通过模糊控制器的模糊化处理以及PID控制器的精确调整,实现对煤矿提升机变频调速的自动控制。实验结果表明,基于模糊自适应PID的煤矿提升机变频调速控制方法,控制波特率均在120bps/h以上,证明该方法对于煤矿提升机变频调速控制具有良好的控制效果,为煤矿提升机的安全、高效运行提供有力保障。