Abstract
In this paper, a novel adaptive fuzzy-neural dynamic surface control (DSC) approach is proposed for a class of single-input and single-output (SISO) uncertain nonlinear strict-feedback systems with unknown time-varying delays and unmeasured states. Fuzzy neural networks are employed to approximate unknown nonlinear functions, and a high-gain filter observer is designed to tackle unmeasured states. Based on the high-gain filter observer, an adaptive output feedback controller is constructed by combining Lyapunov-Krasovskii functions and DSC backstepping technique. The proposed control approach can guarantee all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. The key advantages of our scheme include that (i) the virtual control gains are not constants but nonlinear functions, and (ii) the problem of ”computational explosion” is solved.
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References
Narendra, K.S., Parthasarathy, K.: Identification and Control of Dynamical Systems Using Neural Networks. IEEE Trans. Neural Netw. 1, 4–27 (1990)
Wang, Y.C., Zhang, H.G., Wang, X.Y., Yang, D.S.: Networked synchronization control of coupled dynamic networks with time-varying delay. IEEE Trans. Syst., Man, Cybern. Part B: Cybern. 40, 1468–1479 (2010)
Yang, D.S., Zhang, H.G., Zhao, Y., Song, C.H., Wang, Y.C.: Fuzzy adaptive H ∞ synchronization of time-varying delayed chaotic systems with unknown parameters based on LMI technique. ACTA Physica Sinica 59, 1562–1567 (2010)
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, 195–202 (2005)
Zhang, T.P., Ge, S.S.: Adaptive Neural Network Tracking Control of MIMO Nonlinear Systems with Unknown Dead Zones and Control Directions. IEEE Trans. Neural Netw. 20, 483–497 (2009)
Chen, B., Liu, X.P., Liu, K.F., Lin, C.: Novel Adaptive Neural Control Design for Nonlinear MIMO Time-Delay Systems. Automatica 45, 1554–1560 (2009)
Yip, P.P., Hedrick, J.K.: Adaptive Dynamic Surface Control: A Simplified Algorithm for Adaptive Backstepping Control of Nonlinear Systems. Int. J. Control 71, 959–979 (1998)
Wang, M., Chen, B., Shi, P.: Adaptive Neural Control for a Class of Perturbed Strict-Feedback Nonlinear Time-Delay Systems. IEEE Trans. Syst., Man, Cybern. Part B: Cybern. 38, 721–730 (2008)
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, 2277–2283 (2011)
Krishnamurthy, P., Khorrami, F., Jiang, Z.P.: Global Output Feedback Tracking for Nonlinear Systems in Generalized Output-Feedback Canonical Form. IEEE Trans. Autom. Control 47, 814–819 (2002)
Zhou, J., Er, M.J.: Adaptive Output Control of A Class of Uncertain Chaotic Systems. Syst. Control Lett. 56, 452–460 (2007)
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Li, Y., Li, T., Tong, S. (2012). Adaptive Dynamic Surface Control of Uncertain Nonlinear Time-Delay Systems Based on High-Gain Filter Observer and Fuzzy Neural Networks. 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_46
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DOI: https://doi.org/10.1007/978-3-642-31362-2_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31361-5
Online ISBN: 978-3-642-31362-2
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