title: Tuning fuzzy PD and PI controllers using reinforcement learning.
abstract: In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi-Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen's implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.
authors.affiliation: LAMEL, University of Jijel, BP. 98, Ouled Aissa, 18000, Jijel, Algeria. boubert_hamid@yahoo.com
authors.forename: Hamid
authors.initials: H
authors.lastname: Boubertakh
authors.forename: Mohamed
authors.initials: M
authors.lastname: Tadjine
authors.forename: Pierre-Yves
authors.initials: PY
authors.lastname: Glorennec
authors.forename: Salim
authors.initials: S
authors.lastname: Labiod
mesh_terms: D000401:Aircraft; D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D004867:Equipment Design; D017143:Fuzzy Logic; D007221:Industry / Q000295:instrumentation*; D012054:Reinforcement, Psychology