A closed-loop particle swarm optimizer for multivariable process controller design

Han, Kai; Zhao, Jun; Xu, Zuhua; Qian, Jixin

doi:10.1631/jzus.a0720081

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…To overcome this challenge, tuning of decentralized PI/PID controller by using intelligent optimization techniques such as PSO and GA [11], [12], [13], [14], [15], [16], [17], [18], [19] and [20] are used due to their less complexity, high performance and easy implementation. Genetic algorithms (GAs) belong to the larger class of evolutionary algorithms, which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover [21].…”

Section: Controlling Of Chemical Processes (Which Are Basically Multimentioning

confidence: 99%

An Intelligent Control System Design for an Evaporator based on Particle Swarm Optimization

Abdel-Halim¹,

Othman²,

Sakr³

et al. 2017

IJCA

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The main contribution of this paper is aimed to design and implementation of an intelligent level controller and intelligent 2×2 decentralized PI controller and a lead compensator for the forced circulation evaporator by using PSO strategy. The most important thing to guarantee the safe operation of the forced circulation evaporator, without damaging the installed equipment, is obtaining optimal controllers for the evaporator operating pressure and the level of liquid inside the separator part. Also the percent of the concentration of the non-volatile in the solution must be effectively controlled to required limits. PSO algorithm is implemented in MATLAB and is compared to GA strategy for design and implementation of optimal controllers for the evaporator system by minimizing the summation of the characteristics of unit step response. Also computer simulation results are compared to the different two cost functions methods by analyzing the performance, stability and robustness with respect to variation of the evaporator control system.

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Section: Controlling Of Chemical Processes (Which Are Basically Multimentioning

confidence: 99%

An Intelligent Control System Design for an Evaporator based on Particle Swarm Optimization

Abdel-Halim¹,

Othman²,

Sakr³

et al. 2017

IJCA

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“…In PSO, each particle represents a potential solution. The performance of each particle depends on the fitness value of the objective function [3][4][5]. In every iteration, each particle is updated by the best solution it has so far achieved called personal best and the best solution obtained by the particle swarm called global best.…”

Section: Basis Particle Swarm Optimizationmentioning

confidence: 99%

Smith Prediction Monitor AGC System Based on CPSO Self-Tuning Pi Control

Zhang

Sun

et al. 2013

AMR

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In accordance with the feature of pure delay in monitor AGC system for hot rolling mill, a new self-tuning PI Smith prediction controller based on chaotic particle swarm optimization (CPSO) is developed. The algorithmic principle and design method of new controller are given. Based on the typical monitor AGC model of tandem hot mill, the analysis of dynamic performance for traditional PI Smith prediction controller and CPSO self-tuning PI Smith prediction controller is done by MATLAB. The simulation results indicate that CPSO self-tuning PI Smith prediction controller has faster response and higher static accuracy than traditional PI Smith prediction controller.

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“…Rajabioun et al [10] designed a decentralized PID controller by minimizing total IAE for all loops using colonial competitive algorithm. Han et al [11] tuned PID controller based on a closed loop particle swarm optimizer (PSO) algorithm. Zhao et al [12] minimized integral square error (ISE) employing two-lbests based PSO for designing robust PID controller for MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

Comparative studies on decentralized multiloop PID controller design using evolutionary algorithms

Saha

Das

Pakhira

et al. 2012

2012 Students Conference on Engineering and Systems

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The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication Abstract-Decentralized PID controllers have been designed in this paper for simultaneous tracking of individual process variables in multivariable systems under step reference input. The controller design framework takes into account the minimization of a weighted sum of Integral of Time multiplied Squared Error (ITSE) and Integral of Squared Controller Output (ISCO) so as to balance the overall tracking errors for the process variables and required variation in the corresponding manipulated variables. Decentralized PID gains are tuned using three popular Evolutionary Algorithms (EAs) viz. Genetic Algorithm (GA), Evolutionary Strategy (ES) and Cultural Algorithm (CA). Credible simulation comparisons have been reported for four benchmark 2×2 multivariable processes.

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A closed-loop particle swarm optimizer for multivariable process controller design

Cited by 8 publications

References 20 publications

An Intelligent Control System Design for an Evaporator based on Particle Swarm Optimization

An Intelligent Control System Design for an Evaporator based on Particle Swarm Optimization

Smith Prediction Monitor AGC System Based on CPSO Self-Tuning Pi Control

Comparative studies on decentralized multiloop PID controller design using evolutionary algorithms

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