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Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

Annals of Computer Science and Information Systems, Volume 33

Optimal tracking controllers with Off-policy Reinforcement Learning Algorithm in Quadrotor

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DOI: http://dx.doi.org/10.15439/2022R52

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 29–32 ()

Full text

Abstract. In this study, the optimal tracking control problem for the quadrotor which is a highly coupling system with completely unknown dynamics is addressed based on data by introducing the reinforcement learning (RL) technique. The proposed Off-policy RL algorithm does not need any knowledge of quadrotor model. By collecting data, which is the states of quadrotor system then using an actor-critic networks (NNs) to solve the optimal tracking trajectory problem. Finally, simulation results are provided to illustrate the effectiveness of proposed method.

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