Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor

Ulbio Alejandro-Sanjines; Anthony Maisincho-Jivaja; Victor Asanza; Leandro L. Lorente-Leyva; Diego H. Peluffo-Ordóñez

doi:10.3390/biomimetics8050434

Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor

Journal

Biomimetics

ISSN

2313-7673

Date Issued

2023-09-19

Author(s)

Ulbio Alejandro-Sanjines

Anthony Maisincho-Jivaja

Victor Asanza

Leandro L. Lorente-Leyva

Facultad de Derecho, Ciencias Administrativas y Sociales

Diego H. Peluffo-Ordóñez

DOI

10.3390/biomimetics8050434

URL

https://cris.ute.edu.ec/handle/123456789/330

Abstract

Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its initial conditions. However, if this is not the case, the controller must be retuned, affecting production times. In this work, an adaptive PID controller is developed for a DC motor speed plant using an artificial intelligence algorithm based on reinforcement learning. This algorithm uses an actor–critic agent, where its objective is to optimize the actor’s policy and train a critic for rewards. This will generate the appropriate gains without the need to know the system. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) was used, with a network composed of 300 neurons for the agent’s learning. Finally, the performance of the obtained controller is compared with a classical control one using a cost function.