Abstract
In this paper, we propose an indirect adaptive control scheme using Hopfield-based dynamic neural network for SISO nonlinear systems with external disturbances. Hopfield-based dynamic neural networks are used to obtain uncertain function estimations in an indirect adaptive controller, and a compensation controller is used to suppress the effect of approximation error and disturbance. The weights of Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed. In addition, the tracking error can be attenuated to a desired level by selecting some parameters adequately. Simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.
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© 2009 Springer-Verlag Berlin Heidelberg
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Chen, PC., Wang, CH., Lee, TT. (2009). Indirect Adaptive Control Using Hopfield-Based Dynamic Neural Network for SISO Nonlinear Systems. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_31
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DOI: https://doi.org/10.1007/978-3-642-03969-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03968-3
Online ISBN: 978-3-642-03969-0
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