Abstract
In this paper, we present a new scheme to design direct adaptive neural network controller for uncertain nonlinear systems in the presence of input saturation. By incorporating dynamic surface control (DSC) technique into a neural network based adaptive control design framework, the control design is achieved. With this technique, the problem of “explosion of complexity” inherent in the conventional backstepping method is avoided, and the controller singularity problem is removed, and the effect of input saturation constrains is considered. In addition, it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed scheme.
This work was supported in part by the National Natural Science Foundation of China (Nos.51179019, 60874056, 61074014, 51009017), the Natural Science Foundation of Liaoning Province (No. 20102012), China Postdoctoral Special Science Foundation (No. 200902241) and the Fundamental Research Funds for the Central Universities (No. 2011QN097, 2012QN013).
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References
Krstic, M., Kanellakopoulos, I., Kokotovic, P.V.: Nonlinear and Adaptive Control Design. Wiley, New York (1995)
Yip, P.P., Hedrick, J.K.: Adaptive dynamic surface control: A simplified algorithm for adaptive backstepping control of nonlinear systems. Int. J. Control 71(5), 959–979 (1998)
Wang, D., Huang, J.: Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Trans. Neural Netw. 16(1), 195–202 (2005)
Polycarpou, M.M., Mears, M.J.: Stable adaptive tracking of uncertainty systems using nonlinearly parameterized on-line approximators. Int. J. Control 70(3), 363–384 (1998)
Ge, S.S., Wang, C.: Direct adaptive NN control of a class of nonlinear systems. IEEE Trans. Neural Networks 13(1), 214–221 (2002)
Li, T.S., Li, R.H., Li, J.F.: Decentralized adaptive neural control of nonlinear interconnected large-scale systems with unknown time delays and input saturation. Neurocomputing 74(14-15), 2277–2283 (2011)
Chen, M., Ge, S.S., Choo, Y.: Neural network tracking control of ocean surface vessels with input saturation. In: Proc. of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009, pp. 85–89 (2009)
Li, J.F., Li, T.S.: Design of ship’s course autopilot with input saturation. ICIC Express Letters 5(10), 3779–3784 (2011)
Zhou, J., Er, M.J., Zhou, Y.: Adaptive neural network control of uncertain nonlinear systems in the presence of input saturation. In: ICARCV 2006, pp. 1–5 (2006)
Qu, Z.: Robust Control of Nonlinear Uncertain Systems. Wiley, New York (1998)
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Li, J., Li, T., Li, Y., Wang, N. (2012). Direct Adaptive Neural Dynamic Surface Control of Uncertain Nonlinear Systems with Input Saturation. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_45
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DOI: https://doi.org/10.1007/978-3-642-31362-2_45
Publisher Name: Springer, Berlin, Heidelberg
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