NDLI: Neural network-based command filtered control for induction motors with input saturation

Content Provider	IET Digital Library
Author	Fu, Cheng Zhao, Lin Yu, Jinpeng Yu, Haisheng Lin, Chong
Abstract	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.
Starting Page	2636
Ending Page	2642
Page Count	7
ISSN	17518644
Volume Number	11
e-ISSN	17518652
Issue Number	Issue 15, Oct (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/11/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2017.0059
Journal	IET Control Theory & Applications
Publisher Date	2017-06-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Control Asynchronous Machine Backstepping Design Compensating Signal Control Nonlinearities Control of Electric Power System Control System Analysis Control System Synthesis Dynamic Surface Technology Explosion-of-complexity Problem Induction Motors Input Saturation Machine Control Neural Network Approximation-based Command Filtered Adaptive Control Neurocontrol Neurocontrollers Nonlinear Control System Nonlinearity Approximation Self Adjusting Control System Synthesis Method Virtual Controller
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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