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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Ge, S.S., Hang, C.C., Lee, T.H., Zhang, T.: Stable Adaptive Neural Network Control. Kluwer, Norwell (2001)
Kosmatopoulos, E.B., Polycarpou, M.M., Christodoulou, M.A., Ioannou, P.A.: High-order neural network structures for identification of dynamical systems. IEEE Trans. Neural Netw. 6, 422–431 (1995)
Lewis, F.L., Jagannathan, S., Yesildirek, A.: Neural Network Control of Robot Manipulators and Nonlinear Systems. Taylor and Francis, New York (1999)
White, D.A., Sofge, D.A.: Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Applications. Van Nostrand and Reinhold, New York (1993)
Ge, S.S., Lee, T.H., Harris, C.J.: Adaptive Neural Network Control of Robotic Manipulators. World Scientific, London (1998)
Das, T., et al.: Simple neuron-based adaptive controller for a nonholonomic mobile robot including actuator dynamics. Neurocomput. 69, 2140–2151 (2006)
Yoo, S.J., et al.: Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural network via adaptive learning rates. Inf. Sci. 177, 3074–3098 (2007)
Bugeja, M.K., Fabri, S.G., Camilleri, L.: Dual adaptive dynamic control of mobile robots using neural networks. IEEE Trans. Syst., Man, Cybern. B, Cybern. 39, 129–141 (2009)
Park, B.S., Yoo, S.J., Park, J.B., Choi, Y.H.: Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty. IEEE Trans. Control. Syst. Technol. 17, 207–214 (2009)
Liu, Y., Li, Y.: Sliding mode adaptive neural-network control for nonholonomic mobile modular manipulators. J. Intel. Robot. Syst. 44, 203–224 (2005)
Dierks, T., Jagannathan, S.: Asymptotic Adaptive Neural Network Tracking Control of Nonholonomic Mobile Robot Formations. J. Intel. Robot. Syst. 56, 153–176 (2009)
Chaitanya, V.S.: Full-state tracking control of a mobile robot using neural networks. Int. J. Neur. Syst. 15, 403–414 (2005)
Fierro, R., Lewis, F.L.: Control of a nonholonomic mobile robot using neural networks. IEEE Trans. Neural Netw. 9, 589–600 (1998)
Sousa, C.D., et al.: Adaptive control for mobile robot using wavelet networks. IEEE Trans. Syst. Man, Cybern. B, Cybern. 32, 493–504 (2002)
Yoo, S.J., et al.: Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks. IEEE Trans. Syst., Man, Cybern. B, Cybern. 36, 1342–1355 (2006)
Chen, W.S., Jiao, L.C., Wu, J.S.: Globally stable adaptive robust tracking control using RBF neural networks as feedforward compensators. Neural Comput. Appl. 21, 351–363 (2012)
Krstić, M., Deng, H.: Stabilization of nonlinear uncertain systems. Springer, London (1998)
Ioannou, P.A., Sun, J.: Robust adaptive control. Prentice-hall, Englewood Cliffs (1995)
Polycarpou, M.M.: Stable adaptive neural control schemes for nonlinear systems. IEEE Trans. Automat. Control. 41, 447–451 (1996)
Chen, X., Li, Y.: Smooth formation navigation of multiple mobile robots for avoiding moving obstacles. Int. J. of Contr., Automat. Syst. 4, 466–479 (2006)
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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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