This paper deals with the asymptotic tracking control for the uncertain nonholonomic wheeled mobile robot system subjected to actuator saturation and external disturbances simultaneously. A dynamic system is introduced to deal with the actuator saturation, radial basis function neural networks (RBF NNs) are employed to approximate the unknown closed-loop system dynamics, and an adaptive sliding mode feedback term is used to compensate for the approximation error as well as external disturbances. Consequently, a novel adaptive neural controller is designed to guarantee the stability of the closed-loop system and the asymptotic convergence of tracking errors. Meanwhile, the convergence of NN weights is verified, which means that accurate approximation of the unknown closed-loop system dynamics can be obtained and the constant weights can be reused to perform the same or similar control tasks. Finally, simulation studies illustrate the effectiveness of the proposed scheme.
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The authors thank the associate editor and anonymous reviewers for their useful comments to improve the quality of the manuscript. This work is partially supported by the Guangdong Science and Technology Project under Grant Nos. 2015B010133002 and 2017B090910011.
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The authors declare that they have no conflicts of interest.
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Wu, Y., Wang, Y. Asymptotic tracking control of uncertain nonholonomic wheeled mobile robot with actuator saturation and external disturbances. Neural Comput & Applic 32, 8735–8745 (2020). https://doi.org/10.1007/s00521-019-04373-9
- Asymptotic tracking
- Adaptive neural controller
- Nonholonomic wheeled mobile robot
- Actuator saturation