Journal
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
Volume 357, Issue 14, Pages 9709-9732Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jfranklin.2020.07.037
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Funding
- National Natural Science Foundation of China [61903126]
- Key Scientific Research Project of Colleges and Universities in Henan Province of China [20A590001]
- Natural Science Basic Research Plan in Shaanxi Province of China [2019JQ-035]
- Key R&D and Promotion Project in Henan Province of China [202102210130]
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This paper investigates the neural adaptive tracking control problem of a class of strict-feedback systems considering asymmetric full-state with input magnitude and rate constraint (MRC). By designing a dual-integral-type actual control law, the MRC on system input is transformed to be the magnitude limitations on the extended states of the original system, so the original system with both state and MRC considerations is converted to be a new system with only full-state constraint. Besides, compared with the traditional symmetric integral barrier Lyapunov function, new asymmetric integral barrier Lyapunov function is introduced to the dynamic surface-based controller design process in this paper for dealing with the asymmetric state constraint problem. It is analyzed that the original system is semi-globally uniformly ultimately bounded, and that the desired multiple constraints are never violated. The effectiveness of the control strategy is shown via numerical simulations. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
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