Reinforcement Learning Compensation based PD Control for a Double Inverted Pendulum

Guillermo Puriel Gil, Wen Yu, Humberto Sossa

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

11 Citas (Scopus)

Resumen

In this paper, we present a Control Algorithm based on Reinforcement Learning for a double inverted pendulum on a cart. By implementing the Q-Learning techniques in the PD control scheme, the second pendulum (top pendulum) is enabled to improve its performance. In a first step, Q-Learning is used so that the control can balance the second pendulum towards its inverted vertical position, while the first pendulum has no restrictions on its movement and also the car remains in a range of ±1 meter in its displacement. In a second step, we combine hybrid techniques of Q-Learning and PD control, in a system that has had changes in its parameters and in its initial conditions. Then, with the hybrid control, we obtain better results than using the controllers individually. Finally, the simulation results show the effectiveness of the proposed controller.

Idioma originalInglés
Número de artículo8863179
Páginas (desde-hasta)323-329
Número de páginas7
PublicaciónIEEE Latin America Transactions
Volumen17
N.º2
DOI
EstadoPublicada - feb. 2019
Publicado de forma externa

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