Tuning scaling factors of fuzzy logic controllers via reinforcement learning policy gradient algorithms

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Tavakol Aghaei, Vahid and Onat, Ahmet (2017) Tuning scaling factors of fuzzy logic controllers via reinforcement learning policy gradient algorithms. In: 3rd International Conference on Mechatronics and Robotics Engineering (ICMRE 2017), Paris, France

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Abstract

In this study a gain scheduling method for the scaling factors of the input variables to the fuzzy logic controller by means of policy gradient reinforcement learning algorithms has been proposed. The motivation for using PG algorithms is that they can scale RL problems into continuous high dimensional state-action spaces without the need for function approximation methods. Without incorporating any apriori knowledge of the plant, the proposed method optimizes the cost function of the learning algorithm and tries to find optimal solutions for the scaling factors of the fuzzy logic controller. To show the effectiveness of the proposed method it has been applied to a PD type fuzzy controller along with a nonlinear model of an inverted pendulum. By performing different simulations, it is observed that the proposed method can find optimal solutions within a small number of learning iterations.
Item Type: Papers in Conference Proceedings
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Mechatronics
Faculty of Engineering and Natural Sciences
Depositing User: Vahid Tavakol Aghaei
Date Deposited: 31 Jul 2018 11:19
Last Modified: 26 Apr 2022 09:29
URI: https://research.sabanciuniv.edu/id/eprint/34652

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