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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Veröffentlicht in:	Complexity (New York, N.Y.) Jg. 2022; H. 1
Hauptverfasser:	Zhang, Chen, Qin, Wen, Fan, Ming-Can, Wang, Ting, Shen, Mou-Quan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken Hindawi 2022 John Wiley & Sons, Inc Wiley
Schlagworte:	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-Zugang:	Volltext
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