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A reinforced combinatorial particle swarm optimization based multimodel identification of nonlinear systems

Published online by Cambridge University Press:  05 December 2016

Ahmed A. Adeniran  and
Sami El Ferik
Ahmed A. Adeniran*
Affiliation:
Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Sami El Ferik
Affiliation:
Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
*
Reprint requests to: Ahmed A. Adeniran, Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia. E-mail: [email protected]
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Abstract

Several industrial systems are characterized by high nonlinearities with wide operating ranges and large set point changes. Identification and representation of these systems represent a challenge, especially for control engineers. Multimodel technique is one effective approach that can be used to describe nonlinear systems through the combination of several submodels, where each is contributing to the output with a certain degree of validity. One major concern in this technique, especially for systems with unknown operating conditions, is the partitioning of the system's operating space and thus the identification of different submodels. This paper proposes a three-stage approach to obtain a multimodel representation of a nonlinear system. A reinforced combinatorial particle swarm optimization and hybrid K-means are used to determine the number of submodels and their respective parameters. The proposed method automatically optimizes the number of submodels with respect to the submodel complexity. This allows operating space partition and generation of a parsimonious number of submodels without prior knowledge. The application of this approach on several examples, including a continuous stirred tank reactor, demonstrates its effectiveness.

Keywords

K-MeansMultimodelNonlinear SystemsParticle Swarm OptimizationSystems Identification
Type
Regular Articles
Information
AI EDAM , Volume 31 , Special Issue 3: Uncertainty Quantification for Engineering Design , August 2017 , pp. 327 - 358
Copyright
Copyright © Cambridge University Press 2016 

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