期刊
INFORMATION SCIENCES
卷 468, 期 -, 页码 29-46出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2018.08.029
关键词
Semi-globally practical finite-time stability (SGPFS); Prescribed performance control (PPC); Tracking control; Neural networks (NNs)
资金
- China Scholarship Council [201606080044]
- NSERC of Canada
- Taishan Scholar Project of Shandong Province of China [2015162]
- National Natural Science Funds of China [61773108]
This paper focuses on the semi-globally practical finite-time tracking control problem for a class of nonlinear systems with non-strict feedback structure. Inspired by prescribed performance control (PPC), a new performance function called finite-time performance function (FTPF) is defined for the first time. With the aid of neural networks and backstepping, an adaptive finite-time tracking controller is properly designed. Different from the existing finite-time results, the proposed method can guarantee that the tracking error converges to an arbitrarily small region at any settling time and all the signals in the closed-loop system are semi-globally practical finite-time stable (SGPF-stable). Two simulation examples are given to exhibit the effectiveness and superiority of the presented technique. (C) 2018 Elsevier Inc. All rights reserved.
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