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

Chen Zhang, Wen Qin, Ming-Can Fan, Ting Wang, Mou-Quan Shen and Miaomiao Wang

Complexity, 2022, vol. 2022, 1-19

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5093277

DOI: 10.1155/2022/5093277

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