Escaño J.M. , Bordons C. , Witheephanich K. , GÓMEZ-ESTERN AGUILAR, FABIO
No
Int. J. Fuzzy Syst.
Article
Científica
4.406
0.758
01/01/2019
000493567300002
2-s2.0-85069722057
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.
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