Open AccessEvent‐triggered neural control for non‐strict‐feedback systems with actuator failures
Author(s) -
Xu Yuanyuan,
Zhou Qi,
Li Tieshan,
Liang Hongjing
Publication year - 2019
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.2018.5403
Subject(s) - control theory (sociology) , actuator , computer science , control engineering , feedback control , control (management) , control system , event (particle physics) , output feedback , engineering , artificial intelligence , physics , electrical engineering , quantum mechanics
This study is concerned with an adaptive event‐triggered control problem for non‐linear non‐strict‐feedback systems subject to actuator failures. For actuator failures, both total loss of effectiveness (TLOE) and partial loss of effectiveness (PLOE) are considered. The event‐triggered mechanism is proposed in this study, which may influence measurement errors. Neural networks (NNs) are used to approximate unknown non‐linear functions, and a neural observer is designed to estimate unknown state variables. Then a neural tracking controller is constructed to reduce the communication burden via backstepping technique. The new controller ensures that the output of the system reaches to the same trajectory with the reference signal, and it also guarantees the boundedness of all the closed‐loop signals. Finally, a simulation example is used to testify the results.