Robust adaptive trajectory tracking for wheeled mobile robots based on Gaussian process regression

In this paper, we propose a novel learning-based robust adaptive trajectory tracking controller for wheeled mobile robots (WMR) subject to velocity input uncertainties. Gaussian process regression (GPR) is employed in view of its powerful estimation ability and wide scope of applications as a nonpar...

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Bibliographic Details
Published in:Systems & control letters 2022-05, Vol.163, p.105210, Article 105210
Main Authors: Liu, Dan, Tang, Meiqi, Fu, Junjie
Format: Article
Language:English
Subjects:
Gaussian process regression
Robust trajectory tracking
Uncertainty
Wheeled mobile robot
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Summary:In this paper, we propose a novel learning-based robust adaptive trajectory tracking controller for wheeled mobile robots (WMR) subject to velocity input uncertainties. Gaussian process regression (GPR) is employed in view of its powerful estimation ability and wide scope of applications as a nonparametric regression method. The velocity uncertainties are estimated online using the real-time measured data. The prediction mean and variance of the GPR are used to counteract the effect of the uncertainties and to design the robust control term, respectively. A continuous robust controller is proposed which has the advantage of achieving asymptotic convergence of the trajectory tracking error instead of uniformly ultimately boundedness. Comparisons with the neural network based adaptive controller and a state of the art GPR-based design demonstrate the effectiveness of the proposed control strategy.
ISSN:0167-6911
1872-7956
DOI:10.1016/j.sysconle.2022.105210