Design and implementation of a new fuzzy PID controller for networked control systems

This paper presents a practical network platform to design and implement a networked-based cascade control system linking a Smar Foundation Fieldbus (FF) controller (DFI-302) and a Siemens programmable logic controller (PLC-S7-315-2DP) through Industrial Ethernet to a laboratory pilot plant. In the presented network configuration, the Smar OPC tag browser and Siemens WinCC OPC Channel provide the communicating interface between the two controllers. The paper investigates the performance of a PID controller implemented in two different possible configurations of FF function block (FB) and networked control system (NCS) via a remote Siemens PLC. In the FB control system implementation, the desired set-point is provided by the Siemens Human-Machine Interface (HMI) software (i.e, WinCC) via an Ethernet Modbus link. While, in the NCS implementation, the cascade loop is realized in remote Siemens PLC station and the final element set-point is sent to the Smar FF station via Ethernet bus. A new fuzzy PID control strategy is then proposed to improve the control performances of the networked-based control systems due to an induced transmission delay degradation effect. The proposed strategy utilizes an innovative idea based on sectionalizing the error signal of the step response into three different functional zones. The supporting philosophy behind these three functional zones is to decompose the desired control objectives in terms of rising time, settling time and steady-state error measures maintained by an appropriate PID-type controller in each zone. Then, fuzzy membership factors are defined to configure the control signal on the basis of the fuzzy weighted PID outputs of all three zones. The obtained results illustrate the effectiveness of the proposed fuzzy PID control scheme in improving the performances of the implemented NCS for different transportation delays.     

Application of multi-model switching predictive functional control on the temperature system of an electric heating furnace

A method of multi-model switching based predictive functional control is proposed and applied to the temperature control system of an electric heating furnace. The control strategies provide the effective and independent control modes of the electric heating furnace temperature in order to obtain improved control performance. The method depends on conventional implementation of the multi-model switching state, which requires some endeavors to tune the switching model in the model predictive control and allows a reduction of the calculation compared with the weighted multiple model algorithms. In order to test the advantage of the proposed method, experimental equipment is set up and experiments are done on the temperature process of a heating furnace, which verify the validity and effectiveness of the proposed algorithm.     

A new Smith predictor and controller for control of processes with long dead time

Good control of processes with long dead time is often achieved using a Smith predictor configuration. Typically a PI or PID controller is used; however, it is shown in this paper that for some situations improved set point and disturbance responses can be obtained if a PI-PD controller is used. Several methods are possible for selecting the parameters of the PI-PD controller but when the plant transfer function has no zeros, the use of the standard forms provides a simple algebraic approach, and also reveals why difficulties may be encountered if a PID controller is used. Some examples are given to show the value of the approach presented.     

Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor

In this paper, the authors have represented the nonlinear system as a family of local linear state space models, local PID controllers have been designed on the basis of linear models, and the weighted sum of the output from the local PID controllers (Nonlinear PID controller) has been used to control the nonlinear process. Further, Nonlinear Model Predictive Controller using the family of local linear state space models (F-NMPC) has been developed. The effectiveness of the proposed control schemes has been demonstrated on a CSTR process, which exhibits dynamic nonlinearity.     

基于模糊PID控制器的多电机同步控制装置的应用

该文在PID控制的基础上引入了模糊逻辑的概念,提出了一种较新的模糊PID控制算法,并使之应用在多电机同步控制装置中,实验结果表明该控制器能够大大增强系统的动态特性,使多电机良好地以一定速度同步运行。     

大滞后过程的模糊免疫自整定PID控制

目前大滞后过程的控制问题仍然是控制理论研究的重要课题,本文研究提出了一种模糊免疫PID控制器,针对一阶大时滞模型进行数字仿真。仿真结果证明了该方案在一定的时滞范围内具有很好的动静态响应特性和较强的鲁棒性,为解决大滞后过程的控制问题提供了一种可行的方法。     

Adaptive predictive functional control of a class of nonlinear systems

This paper describes the use of pseudo-partial derivative (PPD) to dynamically linearize a nonlinear system, and aggregation is applied to the predicted PPD, resulting in a model-free adaptive predictive control algorithm for a nonlinear system. The algorithm design is only based on the PPD derived online from the input/output data of the controlled process, however it does provide bounded input/output sequence and setpoint tracking without steady-state error. A detailed discussion on parameter selection is also provided. To show the capability of the algorithm, simulations of a time-delay plant and a pH neutralization process show that the proposed method is effective for system parameter perturbation and external disturbance rejection.     

燧道式炉二自由度PID控制的MATLAB仿真研究

该文利用MATLAB仿真软件对二自由度PID在燧道式炉中的应用进行仿真研究,并给出仿真结果。仿真结果表明,燧道式炉采用二自由度PID控制能够实现给定跟踪性能和扰动抑制性能同时达到最佳。     

电锅炉温度控制系统中的混沌优化PID控制器设计

利用混沌运动的遍历性特点,设计了针对电锅炉温度控制系统的基于混沌优化的PID控制器,仿真结果表明该算法能够实现PID参数的最优整定,具有良好的动态和稳态控制效果。     

预测函数控制在工业锅炉燃烧系统上的应用

通过将预测函数控制应用于链条锅炉燃烧控制的系统仿真,结果表明这种控制器具有较强的适应性和鲁棒性,而互有较高的控制精度。     

PID控制器参数快速整定的新方法

本文分析了影响控制器参数整定的几个基本问题（控制目标、优化准则和被控过程的特性），提出了精确地确定被控过程模型参数的方法。通过计算机仿真，系统地提出了可适用于多容过程的ＰＩＤ控制器参数快速整定新方法。     

Design of a nuclear reactor controller using a model predictive control method

A model predictive controller is designed to control thermal power in a nuclear reactor. The basic concept of the model predictive control is to solve an optimization problem for finite future time steps at current time, to implement only the first optimal control input among the solved control inputs, and to repeat the procedure at each subsequent instant. A controller design model used for designing the model predictive controller is estimated every time step by applying a recursive parameter estimation algorithm. A 3-dimensional nuclear reactor analysis code, MASTER that was developed by Korea Atomic Energy Research Institute (KAERI), was used to verify the proposed controller for a nuclear reactor. It was known that the nuclear power controlled by the proposed controller well tracks the desired power level and the desired axial power distribution.     

Fuzzy logic versus a PID controller for position control of a muscle-like actuated arm

This study compares three different control algorithms for a muscle-like actuated arm developed to replicate motion in two degrees-of-freedom (df): elbow flexion/extension (f/e) and forearm pronation/supination (p/s). Electromyogram (EMG) is employed to help determine the control signal used to actuate the muscle cylinders. Three different types of control strategies were attempted. The first algorithm used fuzzy logic with EMG signals and position error as control inputs (Fuzzy Controller). The second algorithm incorporated moment arm information into the existing fuzzy logic controller (Fuzzy-MA Controller). The third algorithm was a conventional Proportional-Integral-Derivative (PID) controller, which operated solely on position and integration error (PID Controller). Overall, moment arm scaling aided the fuzzy logic control algorithm by improving movement accuracy as determined by relative error and correlation. The PID controller resulted in the most accurate movement tracking after fine tuning the control gains. This study implies that moment arm scaling is an effective tool for improving motion tracking accuracy of the fuzzy controller in the mechanical arm. The study also implies that PID controller can be used as a substitute for the fuzzy based controller once the desired motion is prescribed.     

一种基于粒子群算法的二自由度PID控制器优化设计

粒子群算法在解决复杂问题的优化设计上具有算法简单,计算速度快的优点,将粒子群算法引入到二自由度PID控制器的参数整定中,用于改进二自由度PID控制器参数整定的效率并进行了仿真实验,仿真结果表明,系统同时具有了最优的目标值跟踪特性和干扰抑制特性.     

新型PID参数选取方法在加热炉控制中的应用

加热炉煤气流量控制属于经典过程控制,最适宜的控制方案是PID控制,但利用传统的控制器参数整定方法,目标跟踪特性和干扰抑制特性不能兼顾。该文介绍一种新型PID参数选取方法,经仿真控制效果较好。     

一种PID型自适应模糊控制的设计及其应用

提出了一种PID型自适应模糊控制方法,进而将该方法应用于机器人末端执行器进行控制,仿真结果表明本文提出的控制方案在应用于机器人力控制时具有优秀的响应性能和运动精度,同时该方法简单实用,非常适用于实际生产。     

多变量PI型有约束后移步限预测控制器在水泥窑控制中的仿真研究

本文设计了具有比例积分结构的多变量ＰＩ型有移步限预测控制器,并给出在旋转式水泥窑过程控制中的仿真研究结果。     

多变量PID神经元网络控制系统在集气管压力系统中的应用

针对某焦化厂焦炉集气管压力系统具有强干扰、强耦合、非线性的特点,采用一种多变量PID神经元网络控制算法,更好地实现了解耦控制。这种多变量PID神经元网络与常规的PID控制算法比较不仅具有较好的动态性能和稳态性能,而且还具有很强的自学习功能和自适应解耦能力,适用于对非线性多变量系统的解耦控制。     

基于模糊自整定PID的二质量伺服控制系统的研究

在分析了直流伺服系统电学方程和动力学方程的基础上,建立了二质量伺服系统的数学模型及SIMULINK仿真模型.通过MATLAB模糊工具箱和SIMULINK,实现了模糊自整定PID控制器的设计和系统仿真.仿真结果表明用模糊自整定PID控制二质量伺服系统,系统响应快,超调量小,稳定精度高.     

填料塔热交换系统的模糊模型PID优化控制

建立了填料塔热交换实验装置的计算机控制系统.针对过程的严重非线性与不稳定性,在机理分析的基础上建立了分段非线性模型,提出了一种基于T-S模糊模型的非线性系统PID优化控制算法,设计了该装置的模糊模型PID优化控制器.实验结果与传统的PID控制结果进行对比表明,采用模糊模型PID优化控制方法能够有效解决非线性控制问题,控制效果令人满意.