Explicit simplified MPC with an adjustment parameter adapted by a fuzzy system

In this work, a novel methodology is presented to reduce the computational complexity of applying explicit solution of Model Predictive Control (MPC). The methodology is based on applying the functional principal component analysis, providing a mathematically elegant approach to reduce the complexit...

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Published in:Journal of intelligent & fuzzy systems 2019-01, Vol.37 (1), p.1287-1298
Main Authors: Manuel Escaño, Juan, Sánchez, Adolfo J., Witheephanich, Kritchai, Roshany-Yamchi, Samira, Bordons, Carlos
Format: Article
Language:English
Subjects:
Complexity
Computation
Embedded systems
Fuzzy logic
Fuzzy systems
Predictive control
Principal components analysis
Programmable logic controllers
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description In this work, a novel methodology is presented to reduce the computational complexity of applying explicit solution of Model Predictive Control (MPC). The methodology is based on applying the functional principal component analysis, providing a mathematically elegant approach to reduce the complexity of rule-based systems, like piecewise affine systems, allowing the reduction of the number of consequents and combining and merging the antecedents. Thus, the application of MPC is allowed in systems with low computational requirements, such as programmable logic controllers, embedded systems, etc. The proposed design has been validated using an industrial distiller model.
doi_str_mv 10.3233/JIFS-182743
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subjects Complexity
Computation
Embedded systems
Fuzzy logic
Fuzzy systems
Predictive control
Principal components analysis
Programmable logic controllers
title Explicit simplified MPC with an adjustment parameter adapted by a fuzzy system
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