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Tuning fuzzy PD and PI controllers using reinforcement learning

โœ Scribed by Hamid Boubertakh; Mohamed Tadjine; Pierre-Yves Glorennec; Salim Labiod

Publisher
Elsevier Science
Year
2010
Tongue
English
Weight
725 KB
Volume
49
Category
Article
ISSN
0019-0578

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โœฆ Synopsis

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.

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