Abstract : In this paper, a fuzzy feedback linearization is used to control nonlinear systems described by Takagi-Suengo (T-S) fuzzy systems. In this work, an optimal controller is designed using the linear quadratic regulator (LQR). The well known weighting parameters approach is applied to optimize local and global approximation and modelling capability of T-S fuzzy model to improve the choice of the performance index and minimize it. The approach used here can be considered as a generalized version of T-S method. Simulation results indicate the potential, simplicity and generality of the estimation method and the robustness of the proposed optimal LQR algorithm.
Type de document :
Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.466-475, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_46〉
https://hal.inria.fr/hal-01391348
Contributeur : Hal Ifip
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Soumis le : jeudi 3 novembre 2016 - 11:04:31
Dernière modification le : vendredi 1 décembre 2017 - 01:16:36
Document(s) archivé(s) le : samedi 4 février 2017 - 13:12:13
Agustín Jiménez, Basil Al-Hadithi, Juan Pérez-Oria, Luciano Alonso. Optimal Control Using Feedback Linearization for a Generalized T-S Model. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.466-475, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_46〉. 〈hal-01391348〉
