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https://hdl.handle.net/2440/130450
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Type: | Journal article |
Title: | Control design for uncertain switched nonlinear systems: adaptive neural approach |
Author: | Liu, Z. Shi, P. Chen, B. Lin, C. |
Citation: | IEEE transactions on systems, man, and cybernetics. Systems, 2021; 51(4):2322-2331 |
Publisher: | Institute of Electrical and Electronics Engineers |
Issue Date: | 2021 |
ISSN: | 2168-2216 2168-2232 |
Statement of Responsibility: | Zhiliang Liu, Peng Shi, Bing Chen, Chong Lin |
Abstract: | This paper addresses adaptive neural output feedback control for uncertain nonlinear switched systems. The main difficulty for control design comes from the loss of the precise information on those virtual coefficients of each subsystem. To overcome this difficulty, we give a robust observer design scheme by using convex combination approach. Furthermore, develop an observer-based output feedback control strategy. During the procedure of control design, adaptive neural control approach is used to deal with the unknown nonlinear functions and backstepping technique is employed to construct the ideal control laws. It is shown that the presented control law achieves the control issue of getting small tracking error, meanwhile, ensuring boundedness of all the closed-loop signals. Finally, a simulation example is used to test our results. |
Keywords: | Adaptive neural control; backstepping; purefeedback output structure; switched observer; switched systems |
Rights: | © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. |
DOI: | 10.1109/TSMC.2019.2912406 |
Grant ID: | http://purl.org/au-research/grants/arc/DP170102644 |
Published version: | http://dx.doi.org/10.1109/tsmc.2019.2912406 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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