| Título | Fuzzy Model Predictive Control: Complexity Reduction for Implementation in Industrial Systems |
|---|---|
| Autores | Escaño J.M. , Bordons C. , Witheephanich K. , GÓMEZ-ESTERN AGUILAR, FABIO |
| Publicación externa | No |
| Medio | Int. J. Fuzzy Syst. |
| Alcance | Article |
| Naturaleza | Científica |
| Cuartil JCR | 1 |
| Cuartil SJR | 2 |
| Impacto JCR | 4.406 |
| Impacto SJR | 0.758 |
| Web | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069722057&doi=10.1007%2fs40815-019-00693-z&partnerID=40&md5=1d6e5fb8cb41c5c7e77f2a9f97e04539 |
| Fecha de publicacion | 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. |
| Palabras clave | Controllers; Feedback control; Fuzzy inference; Fuzzy logic; Pilot plants; Predictive control systems; Principal component analysis; Complexity reduction; Functional principal component analysis; Fuzzy logic based control; Fuzzy model predictive control; Industrial processs; Industrial systems; Specific operating point; Takagi Sugeno fuzzy systems; Model predictive control |
| Miembros de la Universidad Loyola |