Online parameter adaptive control of mobile robots based on deep reinforcement learning under multiple optimisation objectives

Fixed control parameters and various optimisation objectives significantly limit the robot control performance. To address such issues, a parameter adaptive controller based on deep reinforcement learning is introduced firstly to adjust control parameters according to the real‐time system state. Fur...

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Bibliographic Details
Published in:Cognitive computation and systems Vol. 6; no. 4; pp. 86 - 97
Main Authors: Sui, Xiuli, Chen, Haiyong
Format: Journal Article
Language:English
Published: Dordrecht John Wiley & Sons, Inc 01.12.2024
Wiley
Subjects:
Adaptation
Control algorithms
Controllers
Deep learning
Design
learning (artificial intelligence)
mobile robots
Neural networks
Objectives
Real time
Reinforcement
Robots
ISSN:2517-7567, 1873-9601, 2517-7567, 1873-961X
Online Access:Get full text
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Summary:Fixed control parameters and various optimisation objectives significantly limit the robot control performance. To address such issues, a parameter adaptive controller based on deep reinforcement learning is introduced firstly to adjust control parameters according to the real‐time system state. Further, multiple evaluation mechanisms are constructed to take account of optimisation objectives so that the controller can adapt to different control performance indexes by different evaluation mechanisms. Finally, the target pedestrian tracking control with mobile robots is selected as the validation case study, and the Proportional‐Derivative Controller is chosen as the foundation controller. Several simulation and experimental examples are designed, and the results demonstrate that the proposed method shows satisfactory performance while taking account of multiple optimisation objectives.
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ISSN:2517-7567
1873-9601
2517-7567
1873-961X
DOI:10.1049/ccs2.12105