Title Fuzzy Model Predictive Control: Complexity Reduction for Implementation in Industrial Systems
Authors Escaño J.M., Bordons C., Witheephanich K., GÓMEZ-ESTERN AGUILAR, FABIO, GÓMEZ-ESTERN AGUILAR, FABIO
External publication No
Means Int. J. Fuzzy Syst.
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 2
JCR Impact 4.40600
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069722057&doi=10.1007%2fs40815-019-00693-z&partnerID=40&md5=1d6e5fb8cb41c5c7e77f2a9f97e04539
Publication date 01/01/2019
ISI 000493567300002
Scopus Id 2-s2.0-85069722057
DOI 10.1007/s40815-019-00693-z
Abstract In this paper, a new fuzzy logic-based control-design technique is presented. The method aims at reducing the complexity of Takagi-Sugeno Fuzzy systems via the reduction of fuzzy rules. This reduction is obtained by finding a function basis via the Functional Principal Component Analysis, and then the model is used for Model Predictive Control (MPC). This procedure is systematic, and eventually leads to feasible low-cost microcontroller-based implementations, which has become a generic need in the era of IoT. In order to validate the results, two experimental setups have been controlled using these principles. The first of these, a mechanical pendulum, presents nonlinear dynamics that suggests the use of linear discrete models at specific operating points. In the second, a pilot plant implementing an industrial process with a chemical reactor and a heat exchanger, presents nonlinear multivariate dynamics that are successfully handled with the Fuzzy MPC Controller. © 2019, Taiwan Fuzzy Systems Association.
Keywords Controllers; Feedback control; Fuzzy inference; Fuzzy logic; Pilot plants; Predictive control systems; Principal component analysis; Complexity reduction; Functional principal component analysis; Fuzz
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