Abstract
A robust adaptive control scheme is presented for a class of uncertain continuous-time multi-input multi-output (MIMO) nonlinear systems. Within these schemes, multiple multi-layer neural networks are employed to approximate the uncertainties of the plant’s nonlinear functions and robustifying control term is used to compensate for approximation errors. All parameter adaptive laws and robustifying control term are derived based on Lyapunov stability analysis so that all the signals in the closed loop are guaranteed to be semi-globally uniformly ultimately bounded and the tracking error of the output is proven to converge to a small neighborhood of zero. While the relationships among the control parameters, adaptive gains and robust gains are established to guarantee the transient performance of the closed loop system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Isidori, A.: Nonlinear Control System. Springer, Berlin (1989)
Sastry, S.S., Isidori, A.: Adaptive Control of Linearizable Systems. IEEE Trans. Automat. Contr. 34, 1123–1131 (1989)
Kanellakopoulos, I., Kokotovic, P.V., Morse, A.S.: Systematic Design of Adaptive Controllers for Feedback Linearizable Systems. IEEE Trans. Automat. Contr. 36, 1241–1253 (1991)
Slotine, J.-J.E., Li, W.: Applied Nonlinear Control. Prentice-Hall, Englewood Cliffs (1991)
Sanner, R., Slotine, J.-J.E.: Gaussian Networks for Direct Adaptive Control. IEEE Trans. Neural Networks 3, 837–863 (1992)
Polycarpou, M.M.: Stable Adaptive Neural Control for Nonlinear Systems. IEEE Trans. Automat. Contr. 41, 447–450 (1996)
Rovithakis, G.A., Christodulou, M.A.: Neural Adaptive Regulation of Unknown Nonlinear Dynamical Systems. IEEE Trans. Syst., Man, Cybern. B 27, 810–822 (1997)
Xu, H., Ioannou, P.A.: Robust Adaptive Control for a Class of MIMO Nonlinear Systems With Guaranteed Error Bounds. IEEE Trans. Automat. Contr. 48, 728–742 (2003)
Ge, S.S., Wang, C.: Adaptive Neural Control of Uncertain MIMO Nonlinear Systems. IEEE Trans. Neural Networks 15, 674–692 (2004)
Lewis, F.L., Liu, K., Yesildirek, A.: Neural Net Robot Controller with Guaranteed Tracking Performance. IEEE Trans. Neural Networks 6, 703–715 (1995)
Cheny, S.-C., Chenz, W.-L.: Adaptive Radial Basis Function Neural Network Control with Variable Variance Parameters. Int. Journal of Systems Science 2, 413–424 (2001)
Spooner, J.T., Passino, K.M.: Stable Adaptive Control Using Fuzzy Systems and Neural Networks. IEEE Trans. Fuzzy Syst. 4, 339–359 (1996)
Zhang, T., Ge, S.S., Hang, C.C.: Design and Performance Analysis of a Direct Adaptive Controller for Nonlinear Systems. Automatica 35, 1809–1817 (1999)
Park, J.H., Huh, S.H., Kim, S.H., Seo, S.J., Park, G.T.: Direct Adaptive Controller for Nonaffion Nonlinear Systems Using Self-Structuring Neural Networks. IEEE Trans. Neural Networks 16, 414–422 (2005)
Narendra, K.S., Annaswamy, A.M.: A New Adaptive Law for Robust Adaptation without Persistent Excitation. IEEE Trans. Automat. Contr. 32, 134–145 (1987)
Taylor, D.: Composite Control of Direct-drive Robots. In: Proceedings of the IEEE Conference on Decision and Control, pp. 1670–1675 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hu, T., Zhu, J., Sun, Z. (2006). Adaptive Neural Control for a Class of MIMO Non-linear Systems with Guaranteed Transient Performance. 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_126
Download citation
DOI: https://doi.org/10.1007/11760023_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)