Open AccessIndirect adaptive neural network dynamic surface control for non‐linear time‐delay systems with prescribed performance and unknown dead‐zone input
Author(s) -
Wu Xiaojing,
Wu Xueli,
Luo Xiaoyuan,
Guan Xinping
Publication year - 2018
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2017.1411
Subject(s) - control theory (sociology) , artificial neural network , dead zone , adaptive control , computer science , control (management) , control engineering , engineering , artificial intelligence , oceanography , geology
The problem of tracking control is considered for a class of non‐linear time‐delay systems in the presence of unknown dead‐zone input and tracking error satisfying the prescribed performance. The new state transformation for tracking error is firstly utilised to ensure that the tracking error is within the prescribed boundaries. At the same time, the indirect adaptive neural networks and a new inequality are used to deal with the unknown non‐linear time‐delay terms and the completely unknown dead‐zone input, respectively. Then, the authors design the controller based on the dynamic surface control technique. By constructing the Lyapunov functional, it is shown that the resulting closed‐loop system is stable in the sense of uniformly ultimate boundedness and that the tracking error converges to a small residual set with the prescribed performance. Finally, simulation examples are given and the results show the effectiveness of the proposed control design method.