Predictive Control of a Robot Manipulator with Deep Reinforcement Learning

Eduardo Bejar, Antonio Moran

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This paper tackles the problem of trajectory following of a two-link rigid robot manipulator. The proposed controller bases its operation on the idea behind preview control in which the control law is divided in two parts: a feedback component that depends only on the present state of the system, and a predictive component that only uses future values of the reference trajectory. In this sense, the designed controller uses for training and control both present and future states of the system. Simulation results when following a test trajectory are presented to validate the proposed method and to show that the proposed controller exhibits better performance with respect to a neurocontroller that does not use a predictive component neither for training nor control.

Idioma originalInglés
Título de la publicación alojada2021 7th International Conference on Control, Automation and Robotics, ICCAR 2021
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas127-130
Número de páginas4
ISBN (versión digital)9781665449861
DOI
EstadoPublicada - 23 abr. 2021
Evento7th International Conference on Control, Automation and Robotics, ICCAR 2021 - Singapore, Singapur
Duración: 23 abr. 202126 abr. 2021

Serie de la publicación

Nombre2021 7th International Conference on Control, Automation and Robotics, ICCAR 2021

Conferencia

Conferencia7th International Conference on Control, Automation and Robotics, ICCAR 2021
País/TerritorioSingapur
CiudadSingapore
Período23/04/2126/04/21

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