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https://hdl.handle.net/2440/95917
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Type: | Journal article |
Title: | Exponential stabilization for sampled-data neural-network-based control systems |
Author: | Wu, Z. Shi, P. Su, H. Chu, J. |
Citation: | IEEE Transactions on Neural Networks and Learning Systems, 2014; 25(12):2180-2190 |
Publisher: | IEEE |
Issue Date: | 2014 |
ISSN: | 2162-237X 2162-2388 |
Statement of Responsibility: | Zheng-Guang Wu, Peng Shi, Hongye Su, and Jian Chu |
Abstract: | This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the design methods of the desired sampled-data three-layer fully connected feedforward neural-network-based controller are established to obtain the largest sampling interval and the smallest upper bound of the cost function. A practical example is provided to demonstrate the effectiveness and feasibility of the proposed techniques. |
Keywords: | Exponentially stable; neural networks; nonlinear systems; sampled-data control |
Rights: | © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. |
DOI: | 10.1109/TNNLS.2014.2306202 |
Grant ID: | http://purl.org/au-research/grants/arc/DP140102180 |
Published version: | http://dx.doi.org/10.1109/tnnls.2014.2306202 |
Appears in Collections: | Aurora harvest 3 Electrical and Electronic Engineering publications |
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