Neural network‐based command filtered control for induction motors with input saturation. Issue 15 (1st October 2017)

• Record Type: Journal Article
• Authors: Fu, Cheng; Zhao, Lin; Yu, Jinpeng; Yu, Haisheng; Lin, Chong
• Is Part Of: IET control theory & applications. Volume 11:Issue 15(2017)
• Page Start: 2636
• Page End: 2642
• Publication Date: 2017-10-01
• Subjects: neurocontrollers -- induction motors -- control nonlinearities -- adaptive control -- control system synthesis -- machine control; dynamic surface technology -- compensating signals -- backstepping design -- virtual controllers -- explosion‐of‐complexity problem -- nonlinearity approximation -- neural network approximation‐based command filtered adaptive control -- input saturation -- induction motors; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2017.0059
• Languages: English
• ISSNs: 1751-8644

Abstract

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.