Parameter optimization of MIMO fuzzy optimal model predictive control by APSO
type: Item
instance of: scholarly article
title: Parameter optimization of MIMO fuzzy optimal model predictive control by APSO
zbMATH Open document ID: 1377.93096
DOI: 10.1155/2017/5813192
author: Adel Taieb
author: Moêz Soltani
author: Abdelkader Chaari
published in: Complexity
publication date: 2018-01-05T00:00:00Z
review text: Summary: This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO) Fuzzy Optimal Model Predictive Control (FOMPC) using the Adaptive Particle Swarm Optimization (APSO) algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS) fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR) and Tank system, where the proposed approach provides better performances compared with other methods.
Mathematics Subject Classification ID: 93C42
Mathematics Subject Classification ID: 93C40
Mathematics Subject Classification ID: 93C35
Mathematics Subject Classification ID: 93B40
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 6822318
zbMATH Keywords: Fuzzy Optimal Model Predictive Control (FOMPC)
zbMATH Keywords: multi-input multi-output system
zbMATH Keywords: Takagi-Sugeno (TS) fuzzy system
zbMATH Keywords: adaptive particle swarm optimization (APSO) algorithm
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/5813192
OpenAlex ID: W2762003472
cites work: Model predictive control based on chaos particle swarm optimization for nonlinear processes with constraints
cites work: Fuzzy identification of systems and its applications to modeling and control
cites work: Fuzzy model predictive control of non-linear processes using genetic algorithms.
cites work: Improved particle swarm optimization combined with chaos
description: scientific article; zbMATH DE number 6822318