A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems

This paper proposes an adaptive formation tracking control algorithm optimized by Q-learning scheme for multiple mobile robots. In order to handle the model uncertainties and external disturbances, a desired linear extended state observer is designed to develop an adaptive formation tracking control...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) Vol. 2022; no. 1
Main Authors: Zhang, Chen, Qin, Wen, Fan, Ming-Can, Wang, Ting, Shen, Mou-Quan
Format: Journal Article
Language:English
Published: Hoboken Hindawi 2022
John Wiley & Sons, Inc
Wiley
Subjects:
Adaptive algorithms
Adaptive control
Algorithms
Bandwidths
Control stability
Control systems
Control theory
Controllers
Design
Distance learning
Feedback control
Graph theory
Machine learning
Methods
Multiple robots
Process parameters
Questions and answers
Robot control
Robotics industry
Robots
Sliding mode control
State observers
Tracking control
Tracking errors
ISSN:1076-2787, 1099-0526
Online Access:Get full text
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Summary:This paper proposes an adaptive formation tracking control algorithm optimized by Q-learning scheme for multiple mobile robots. In order to handle the model uncertainties and external disturbances, a desired linear extended state observer is designed to develop an adaptive formation tracking control strategy. Then an adaptive method of sliding mode control parameters optimized by Q-learning scheme is employed, which can avoid the complex parameter tuning process. Furthermore, the stability of the closed-loop control system is rigorously proved by means of matrix properties of graph theory and Lyapunov theory, and the formation tracking errors can be guaranteed to be uniformly ultimately bounded. Finally, simulations are presented to show the proposed algorithm has the advantages of faster convergence rate, higher tracking accuracy, and better steady-state performance.
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ISSN:1076-2787
1099-0526
DOI:10.1155/2022/5093277