Abstract
In this paper, the global tracking control problem for a class of wheeled mobile robots is considered and a new adaptive position tracking control scheme is proposed where radial basis function (RBF) neural network (NN) is utilized to model the uncertainty. The feedback compensation scheme is obtained, where the information of reference position and real position of robot are both used as the NN input. Compered with the existing results, the main advantage is that the global stability of the closed-loop system can be ensured and the NN approximation domain can be determined based on the reference signal a prior. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed control scheme.
This work is supported by National Natural Science Foundation of China (61174213,61203074), the Program for New Century Excellent Talents in University (NCET-10-0665).
Fundamental Research Funds for the Central Universities (K5051370014).,
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Wu, J., Zhao, D., Chen, W. (2013). Global Tracking Control of a Wheeled Mobile Robot Using RBF Neural Networks. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_17
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DOI: https://doi.org/10.1007/978-3-642-39068-5_17
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