Abstract
The problem of direct adaptive neural control for a class of nonlinear systems with an unknown gain sign and nonlinear uncertainty is discussed in this paper. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks (MNNs), and using Nussbaum-type function, a novel design scheme of direct adaptive neural control is proposed. By adopting the adaptive compensation term of the upper bound function of the sum of residual and approximation error, the closed-loop control system is shown to be globally stable, with tracking error converging to zero. Simulation results show the effectiveness of the proposed approach.
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References
Wang, L.X.: Adaptive Fuzzy Systems and Control –Design and Stability Analysis. Prentice Hall, New Jersey (1994)
Sanner, R.M., Slotine, J.J.E.: Gaussian networks for direct adaptive control. IEEE Trans. on Neural Networks 3(6), 837–863 (1992)
Chen, S.C., Chen, W.L.: Adaptive radial basis function neural network control wit variable parameters. Int. J. Systems Science 32(4), 413–424 (2001)
Zhang, T., Ge, S.S., Hang, C.C.: Design and performance analysis of a direct adaptive control for nonlinear systems. Automatica 35(11), 1809–1817 (1999)
Zhang, T., Ge, S.S., Hang, C.C.: Adaptive neural network control for strict-feedback nonlinear systems using back stepping design. Automatica 36(10), 1835–1846 (2000)
Zhang, T., Ge, S.S., Hang, C.C.: Stable adaptive control for a class of nonlinear systems using a modified Lyapunov function. IEEE Trans. on Automatic Control 45(1), 129–132 (2000)
Ge, S.S., Hang, C.C., Zhang, T.: A direct approach to adaptive controller design and its application to inverted pendulum tracking. In: Proceedings of the American Control Conference, Philadelphia, pp. 1043–1047 (1998)
Zhang, T.P., Yang, Y.Q., Zhang, H.Y.: Direct adaptive sliding mode control with nonlinearly parameterized fuzzy approximates. In: Proceedings of 4th World Congress on Intelligent Control and Automation, Shanghai, vol. 3, pp. 915–1919 (2002)
Ge, S.S., Wang, J.: Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems. IEEE Trans on Neural Network 13(6), 1409–1419 (2002)
Ge, S.S., Hong, F., Lee, T.H.: Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients. IEEE Trans on System, Man and Cybernetics-Part B 31(1), 499–516 (2004)
Jiang, W.: Hybird Genetic algorithm research and its application in problem optimization. In: Proceedings of 2004 International Conference on Manchine Learning and Cybernetics, pp. 222–227 (2004)
Jiang, W.: Research on Extracting Medical Diagnosis Rules Based on Rough Sets Theory. Journal of computer science 31(11), 93–96 (2004)
Jiang, W.: Research on Optimize Prediction Model and Algorithm about Chaotic Time Series. Wuhan University Journal of Natural Sciences 9(5), 735–740 (2004)
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Jiang, W., Xu, Y., Xu, Y. (2005). Research on a Direct Adaptive Neural Network Control Method of Nonlinear Systems. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_42
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DOI: https://doi.org/10.1007/11539087_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
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