Abstract
In this paper, by employing Radial Basis Function (RBF) Neural Networks (NN) to approximate uncertain functions, the robust adaptive neural networks design for a class of SISO systems was brought in based on dynamic surface control (DSC) and minimal-learning-parameter (MLP) algorithm. With less learning parameters and reduced computation load, the proposed algorithm can avoid the possible controller singularity problem and the trouble caused by "explosion of complexity" in traditional backstepping methods is removed, so it is convenient to be implemented in applications. In addition, it is proved that all the signals of the closed-loop system are uniformly ultimately bounded(UUB), and simulation results on ocean-going training ship ’YULONG’ are shown to validate the effectiveness and the performance of the proposed algorithm.
Keywords
This work was supported in part by the National Natural Science Foundation of China (No.51179019), the Natural Science Foundation of Liaoning Province (No. 20102012) and the Program for Liaoning Excellent Talents in University (LNET).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Krstic, M., Kanellakopoulos, I., Kokotovic, P.V.: Nonlinear and Adaptive Control Design. Wiley, New York (1995)
Krstic, M., Kanellakopoulos, I., Kokotovic, P.V.: Adaptive nonlinear control without over parameterization. Syst. Control Lett. 19(3), 177–185 (1992)
Kanellakopoulos, I.: Passive adaptive control of nonlinear systems. Int. J. Adapt. Control Signal Process. 7(5), 339–352 (1993)
Fischle, K., Schroder, D.: An improved stable adaptive fuzzy control method. IEEE Trans. Fuzzy Syst. 7(1), 27–40 (1999)
Yang, Y.S., Ren, J.S.: Adaptive fuzzy robust tracking controller design via small gain approach and its application. IEEE Trans. Fuzzy Syst. 11(6), 783–795 (2003)
Yang, Y., Li, T., Wang, X.: Robust adaptive neural network control for strict-feedback nonlinear systems via small-gain approaches. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 888–897. Springer, Heidelberg (2006)
Wang, D., Huang, J.: Neural Network-Based Adaptive Dynamic Surface Control for a Class of Uncertain Nonlinear Systems in Strict-Feedback Form. IEEE Trans. on Neural Networks 16(1), 195–202 (2005)
Li, T.S., Yu, B., Hong, B.G.: A Novel Adaptive Fuzzy Design for Path Following for Underactuated Ships with Actuator Dynamics. In: ICIEA 2009, pp. 2796–2800 (2009)
Liu, C., Li, T.S., Chen, N.X.: Dynamic surface control and minimal learning parameter(DSC-MLP)design of a ship’s autopilot with rudder dynamics. Journal of Harbin Engineering University 33(1), 10–14 (2012)
Li, T.S., Li, W., Luo, W.L.: DSC approach to robust adaptive NN tracking control for a class of MIMO systems. Int. J. Modeling, Identification and Control 11 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, W., Ning, J., Yu, R. (2013). DSC Approach to Robust Adaptive NN Tracking Control for a Class of SISO Systems. 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_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-39068-5_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39067-8
Online ISBN: 978-3-642-39068-5
eBook Packages: Computer ScienceComputer Science (R0)