Nonlinear system PID-type multi-step predictive control

A compound neural network was constructed during the process of identification and multi-step prediction. Under the PID-type long-range predictive cost function, the control signal was calculated based on gradient algorithm. The nonlinear controller’s structure was similar to the conventional PID controller. The parameters of this controller were tuned by using a local recurrent neural network on-line. The controller has a better effect than the conventional PID controller. Simulation study shows the effectiveness and good performance.

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.

Tracking Control of Multi-Agent Systems Using a Networked Predictive PID Tracking Scheme

With the rapid development of network technology and control technology,a networked multi-agent control system is a key direction of modern industrial control systems,such as industrial Internet systems.This paper studies the tracking control problem of networked multi-agent systems with communication constraints,where each agent has no information on the dynamics of other agents except their outputs.A networked predictive proportional integral derivative(PPID)tracking scheme is proposed to achieve the desired tracking performance,compensate actively for communication delays,and simplify implementation in a distributed manner.This scheme combines the past,present and predictive information of neighbour agents to form a tracking error signal for each agent,and applies the proportional,integral,and derivative of the agent tracking error signal to control each individual agent.The criteria of the stability and output tracking consensus of multi-agent systems with the networked PPID tracking scheme are derived through detailed analysis on the closed-loop systems.The effectiveness of the networked PPID tracking scheme is illustrated via an example.

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.

Study of improved gray predictive PID control algorithm

According to these characteristics of the movement of the special platform servo,a new improved grey predictive PID control algorithm was proposed based on the grey predictive PID,and then the algorithm was simulated by MATLAB.As a result that it can improve the response speed and stability of the system,and meet the demand of the system.

A novel flow control scheme for reciprocating compressor based on adaptive predictive PID control

In the process of capacity regulation of reciprocating compressor, the frequent change of inlet temperature and pressure makes the control of exhaust flow unstable, resulting in the high pressure ratio of the intermediate stage. At last the compressor cannot operate safely. To solve the problem, a novel flow control scheme based on inlet temperature and pressure ratio is proposed. In this scheme, the intake model of the cylinder under the capacity regulation condition is established to calculate the load of the first cylinder. Then, the adaptive predictive PID(APPID) controller is designed to control the pressure ratio of other stages, and the grey prediction model is used to predict the pressure output to overcome the system delay. To solve the problem of control parameters tuning, an improved particle swarm optimization(PSO) algorithm is adopted to obtain the optimal control parameters.The effectiveness of the adaptive predictive PID control method is verified by a two-stage compressor model simulation.Finally, the flow control scheme is applied to the actual four-stage air reciprocating compressor flow control system. Although the temperature difference is greater than 15 ℃, the compressor exhaust flow is maintained at the set value and the pressure ratio is also maintained stable. At the same time, the compressor pressure ratio can be quickly adjusted without overshoot. The application result further verifies the feasibility and effectiveness of the scheme.

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.

APPLICATION OF PREDICTIVE CONTROL ALGORITHM IN SPEED CONTROL SYSTEM

In view of DC speed control system, this paper presents a predictive control algorithm to replace traditional PID control. System predictive model requires little information of the controlled object, and because it adopts rolling optimum method, system

Multivariable PI Type Generalized Predictive Control

This paper presents a multivariable generalized predictive controller with proportion and integration structure by modifying the quadratic criterion of the usual MGPC. The control performance has been improved greatly. The effectiveness of the controller is demonstrated by the simulation result.

Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks

After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks is introduced, an intelligent FID controller is adopted to correct the errors including identified model errors and accumulated errors produced in the recursive process. Characterized by predictive control, this method can achieve a good control accuracy and has good robustness. A simulation study shows that this control algorithm is very effective.

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.

双缸液压驱动系统灰色预测模糊PID控制研究

为了提高双缸液压驱动系统非线性变化力的跟踪精度,使驱动力变化相对稳定,设计了灰色预测模糊比例积分微分(PID)控制器,并对驱动力跟踪效果进行仿真验证。为了对双缸液压驱动系统工作原理进行介绍,绘制了双缸液压驱动系统装置简图。针对传统PID控制器进行改进,结合模糊推理和灰色预测理论,设计出灰色模糊PID控制器,并且给出了双缸液压驱动系统控制过程。利用灰色补偿提高控制器的抗干扰能力,采用Matlab软件对双缸液压驱动系统非线性力的变化跟踪进行仿真,与改进前的控制效果进行对比。结果显示:在无干扰环境中,采用PID控制、模糊PID控制和灰色预测模糊PID控制方法,均能提高双缸液压驱动系统力的变化跟踪精度,这3种控制器控制效果相差不大。在有干扰环境中,灰色预测模糊PID控制方法明显优于PID控制和模糊PID控制方法。双缸液压驱动系统采用灰色预测模糊PID控制方法,能够抑制外界波形的干扰,从而提高控制系统的稳定性。

AUTOMATIC TUNING OF PID CONTROLLERS IN THE THERMAL POWER UNIT LOAD CONTROL SYSTEM

A method of measuring the interactions in a multivariable control sys-tem(MVCS)in time domain is defined in this paper.An intelligent decoupling com-pensator is designed in terms of the concept of fuzzy control,so that the auto-tuningof controllers’ parameters in a 2×2 MVCS can be turned into that of two independentsingle-loop control systems(SLCS).The method presented in the paper has success-fully been used in a simulation experiment on the automatic tuning of a coordinatedcontrol system(CCS)in the drum-boiler turbogenerating unit(DBTU)and the simu-lation results axe satisfactory.

Grey Prediction Fuzzy Control of the Target Tracking System in a Robot Weapon

Grey modeling can be used to predict the behavioral development of a system and find out the lead control values of the system. By using fuzzy inference, PID parameters can be adjusted on line by the fuzzy controller with PID parameters self-tuning. According to the characteristics of target tracking system in a robot weapon, grey prediction theory and fuzzy PID control ideas are combined. A grey prediction mathematical model is constructed and a fuzzy PID controller with grey prediction was developed. Simulation result shows fuzzy PID control algorithm with grey prediction is an efficient method that can improve the control equality and robustness of traditional PID control and fuzzy PID control, and has much better performance for target tracking.

Motion Control of Omnidirectional Mobile Platform based on Fuzzy PID

The omnidirectional mobile platform has three degrees of freedom that make it widely applicable to all areas of industry, while the Mecanum wheel has the disadvantages wheel omnidirectional mobile platform is always difficult in of large vibration, the trajectory precision of Mecanum the omnidirectional mobile platform. To control the trajectory of the omnidirectional mobile platform better, this paper proposes a fuzzy self-tuning PID control model, through establishing the motion model of omnidirectional mobile platform in Adams software, then combined with Simulink simulation, analysis of fuzzy PID controller to improve the accuracy of the speed control of omnidirectional mobile platform, improve the control method of a precise trajectory of the omnidirectional mobile platform motion.

温室电动拖拉机旋耕稳定性时序分析与前馈PID控制方法研究

针对温室小型农机对地面平整度敏感,微小的地面起伏便会造成机具俯仰的情况,基于课题组已开发的温室电动拖拉机,将基于时间序列分析的角度预测方法引入前馈PID控制(Angle prediction and feedforward PID,APF-PID),解决了温室旋耕作业中因机具俯仰而出现的响应性差、耕深不稳定和功率突变的问题。建立了温室电动拖拉机旋耕作业的功率模型,并建立了俯仰角-耕深的转换矩阵,得到了旋耕系统实际耕深的转换值;采用时间序列分析预测机身俯仰角,并作为旋耕系统的扰动输入;结合耕深的转换值和预测得到的扰动,采用APF-PID控制器调节旋耕系统的提升机构,将旋耕机维持在目标耕深;在温室内未旋耕和已旋耕的两种地块进行实车试验。结果表明:俯仰角时序预测模型的相关系数可达0.983 2;APF-PID控制的控制性能优于PID控制,在目标耕深6 cm的测试路面中,APF-PID在两种试验地块上的平均耕深分别为6.47 cm和6.44 cm,均方根误差为0.80 cm和0.72 cm,绝对平均误差为0.67 cm和0.58 cm,耕深稳定性系数为89.95%和91.30%,消耗的总能量较PID控制分别降低4.18%和19.13%,较好地实现了温室电动拖拉机旋耕稳定性控制,满足温室作业需求。

基于预测自适应PID控制的家禽养殖基地温度控制系统设计

家禽养殖时,影响家禽生存状态的因素很多,其中最直接的因素是温度。由于温度控制的滞后性比较大,温度控制的要求不同,控制参数和设定要求存在差异。针对不同环境下的控制要求,结合普通PID的控制调节方式,提出一种能普遍适应的控制方法。其核心是基于常规PID的控制基础建立预测自适应控制模型,实现对控制系统中温度的预测自适应PID调节。该方法将模型预测所得的未来一段时间内系统输出值与期望值的均方误差作为性能指标,以间接的方式动态调节PID参数,控制精度较高,充分发挥了PID方法的鲁棒性。所提方法实现了温度的精准控制,能够改善家禽的生活环境,实现高效的无人化管理环境,对于实际的家禽饲养提供参考。

基于MPC和PID的脚轮式全向移动平台轨迹跟踪

针对现有运动控制策略无法保证独立驱动脚轮式全向移动平台位姿的高精度控制问题,提出一种基于模型预测控制(MPC)和PID控制相结合的双闭环轨迹跟踪控制策略。首先,利用运动学几何关系建立独立驱动脚轮式全向移动平台在世界坐标系下的三自由度运动学模型,基于正交分解法建立平台在机器人坐标系下的逆运动学模型,以反映平台中心点速度与各个脚轮转速间的关系;其次,采用MPC并基于三自由度运动学模型设计位姿控制器,使平台对期望轨迹进行位姿跟踪,并在考虑多目标约束条件的情况下通过位姿控制器求解出最优控制量;最后,采用PID设计速度控制器,用于跟踪位姿控制器输出的期望速度,通过平台逆运动学模型计算得到期望轮速,从而驱动平台实现全向运动。通过仿真验证了所提控制策略的有效性,平台能有效跟踪直线轨迹和圆形轨迹。仿真结果表明,与通过平台转角逆运动学模型解耦驱动轮速的位置单环轨迹跟踪控制策略相比,加入速度内环后系统超调量下降97.23%,响应时间缩短36.84%。

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.

基于Smith预估模糊PID的浮选液位控制系统设计

浮选机内液位高低影响着浮选效果和生产稳定性,针对煤泥浮选中浮选机液位对浮选结果产生的影响,结合浮选液位控制系统的特点及其性能要求,采用了一种将Smith预估控制、模糊控制、PID控制结合的液位控制系统,并利用MATLAB中Simulink模块设计浮选液位仿真模型,进行仿真分析,同时改变液位以测试系统的稳定性。通过对传统PID、模糊PID及Smith预估模糊PID控制算法进行比较可知,液位控制系统采用Smith预估模糊PID算法,其控制效果由于传统PID和模糊PID控制,调节时间更短,稳定性更强,提高了浮选机液位的稳定性,能更好地满足浮选过程对液位的要求。