Abstract
Unmanned ground vehicles (UGVs) play an increasingly important role in future space exploration and battlefield. This work is concerned with the automatic path tracking control of UGVs. By using the structure properties of the system, neuro-adaptive control algorithms are developed for high precision tracking without involving complex design procedures – the proposed control scheme only demands partial information of the system, no detail description of the system model is needed. Furthermore, uncertain effects such as external disturbance and uncertain parameters can easily be handled. In addition, all the internal signals are uniformly bounded and the control torque is smooth anywhere.
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© 2006 Springer-Verlag Berlin Heidelberg
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Liao, X., Sun, Z., Weng, L., Li, B., Song, Y., Li, Y. (2006). Adaptive Neural Network Path Tracking of Unmanned Ground Vehicle. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_179
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DOI: https://doi.org/10.1007/11760023_179
Publisher Name: Springer, Berlin, Heidelberg
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