Fuzzy issues in multivariable predictive control

L.F. Mendonça, J.M. Costa Sousa, da, U. Kaymak, J.M. Sá da Costa

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)

Abstract

Model predictive control (MPC) is a well-known control technique, which has been applied to complex and nonlinear processes. This paper integrates different fuzzy issues in multivariable predictive control. Fuzzy predictive control incorporates fuzzy goals and constraints in model predictive control, in a fuzzy decision making framework. Several issues are proposed in this paper for multivariable fuzzy predictive control, namely, the use of weighted fuzzy decision functions and fuzzy predictive filters. Simultaneous weighted satisfaction of various criteria is modeled by using the qualitative extensions of (Archimedean) fuzzy t-norms. The use of fuzzy predictive filters are represented as an adaptive set of control actions multiplied by gain factors. The integration of the several fuzzy issues proposed in this paper is applied to the control of a container gantry crane. Simulation results show the advantages of the proposed methods.
Original languageEnglish
Title of host publication12th IEEE International Conference on Fuzzy Systems, 2003 (FUZZ'03), 25-28 May 2003, St. Louis
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages506-511
Volume1
ISBN (Print)0-7803-7810-5
DOIs
Publication statusPublished - 2003
Externally publishedYes

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