Open AccessNeural network‐based command filtered control for induction motors with input saturation
Author(s) -
Fu Cheng,
Zhao Lin,
Yu Jinpeng,
Yu Haisheng,
Lin Chong
Publication year - 2017
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.2017.0059
Subject(s) - control theory (sociology) , induction motor , artificial neural network , control engineering , computer science , saturation (graph theory) , control (management) , engineering , artificial intelligence , mathematics , voltage , electrical engineering , combinatorics
In this study, neural networks approximation‐based command filtered adaptive control is studied for induction motors with input saturation. The neural networks are utilised to approximate the non‐linearities, and the command filtering technology is used to deal with the ‘explosion of complexity’ problem caused by the derivative of virtual controllers in the conventional backstepping design. The compensating signals are further exploited to get rid of the drawback caused by the dynamics surface technology. It is verified that the adaptive neural controller guarantees that the tracking error can converge to a small neighbourhood of the origin. At last, the effectiveness and advantages of the proposed method are intuitively illustrated by simulation results.