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Neural Computing and Applications

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01-10-2013 | Original Article

Intelligence computation based on adaptive tracking design for a class of non-linear discrete-time systems

Authors: Lei Liu, Yan-Jun Liu, Dong-Juan Li

Published in: Neural Computing and Applications | Issue 5/2013

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Abstract

In this article, a direct adaptive neural networks control algorithm is presented for a class of SISO discrete-time systems with non-symmetric dead-zone. The property of the dead-zone is discretized. Mean value theorem is used to transform the systems into a special form. The unknown functions in the input–output model are approximated using the radial basis function neural networks. Compared with the results for the discrete non-symmetric dead-zone, this article presents a new algorithm to reduce the computational burden. Lyapunov analysis method is utilized to prove that all the signals in the closed-loop systems are semi-global uniformly ultimately bounded. The tracking error is proved to converge to a small set around the zero. A simulation example provided to illustrate the effectiveness of the control schemes.

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Title: Intelligence computation based on adaptive tracking design for a class of non-linear discrete-time systems
Authors: Lei Liu
Yan-Jun Liu
Dong-Juan Li
Publication date: 01-10-2013
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 5/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1080-5

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