Regular Papers

International Journal of Control, Automation and Systems 2022; 20(5): 1632-1639

Published online April 21, 2022

https://doi.org/10.1007/s12555-021-0212-z

© The International Journal of Control, Automation, and Systems

Trajectory Tracking Control for Wheeled Mobile Robots with Kinematic Parameter Uncertainty

Jianjun Bai*, Jian Du, Tianlong Li, and Yun Chen

Hangzhou Dianzi University

Abstract

The trajectory tracking control for a class of wheeled mobile robots (WMRs) with kinematic parameter uncertainty is studied in this paper. Firstly, a new Lyapunov function is proposed, based on which, a new kinematic controller for WMRs without parameter uncertainty is designed to make the tracking error system asymptotically stable. Secondly, the parameter uncertainty of the kinematic model is taken into consideration and an modified adaptive law is designed to deal with the uncertainty. Then, in order to improve the tracking performance, a selftuning algorithm of the parameters of the controllers is proposed based on neural networks. Finally, numerical examples are given to illustrate the effectiveness of the proposed method.

Keywords Adaptive control, kinematic model uncertainty, self-tuning, wheeled mobile robot.

Article

Regular Papers

International Journal of Control, Automation and Systems 2022; 20(5): 1632-1639

Published online May 1, 2022 https://doi.org/10.1007/s12555-021-0212-z

Copyright © The International Journal of Control, Automation, and Systems.

Trajectory Tracking Control for Wheeled Mobile Robots with Kinematic Parameter Uncertainty

Jianjun Bai*, Jian Du, Tianlong Li, and Yun Chen

Hangzhou Dianzi University

Abstract

The trajectory tracking control for a class of wheeled mobile robots (WMRs) with kinematic parameter uncertainty is studied in this paper. Firstly, a new Lyapunov function is proposed, based on which, a new kinematic controller for WMRs without parameter uncertainty is designed to make the tracking error system asymptotically stable. Secondly, the parameter uncertainty of the kinematic model is taken into consideration and an modified adaptive law is designed to deal with the uncertainty. Then, in order to improve the tracking performance, a selftuning algorithm of the parameters of the controllers is proposed based on neural networks. Finally, numerical examples are given to illustrate the effectiveness of the proposed method.

Keywords: Adaptive control, kinematic model uncertainty, self-tuning, wheeled mobile robot.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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