Heuristic and deep reinforcement learning-based PID control of trajectory tracking in a ball-and-plate system

Heuristic and deep reinforcement learning-based PID control of trajectory tracking in a ball-and-plate system

The manual tuning of controller parameters, for example, tuning proportional integral derivative (PID) gains often relies on tedious human engineering. To curb the aforementioned problem, we propose an artificial intelligence-based deep reinforcement learning (RL) PID controller (three variants) com...

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Bibliographic Details
Main Authors: Emmanuel Okafor, Daniel Udekwe, Yusuf Ibrahim, Muhammed Bashir Mu'azu, Ekene Gabriel Okafor
Format: Article
Language:English
Published: Taylor & Francis Group 2021-04-01
Series:Journal of Information and Telecommunication
Subjects:
ball-and-plate
pid controller
deep reinforcement learning
trajectory tracking
genetic algorithm
Online Access:http://dx.doi.org/10.1080/24751839.2020.1833137

Internet

http://dx.doi.org/10.1080/24751839.2020.1833137

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