NDLI: Robust inverse control for PMLSM drives using self-adaptive interval type-2 neural fuzzy network

Content Provider	IEEE Xplore Digital Library
Author	Chaio-Shiung Chen Yung-Sheng Wang
Copyright Year	2013
Description	Author affiliation: Dept. of Mech. & Autom. Eng., Da Yeh Univ., Changhua, Taiwan (Chaio-Shiung Chen; Yung-Sheng Wang)
Abstract	This paper proposes a self-adaptive interval type-2 neural fuzzy network (SAIT2NFN) control system for the high-precision motion control of permanent magnet linear synchronous motor (PMLSM) drives. The SAIT2NFN is firstly trained to model the inverse dynamics of PMLSM through concurrent structure and parameter learning. The fuzzy rules in the SAIT2NFN can be generated automatically by using online clustering algorithm to obtain a suitable-sized network structure, and a back propagation is proposed to adjust all network parameters. Then, a robust SAIT2NFN inverse control system that consists of the SAIT2NFN and an error-feedback controller is proposed to control the PMLSM drive in a changing environment. Moreover, the Kalman filtering algorithm with a dead zone is derived using Lyapunov stability theorem for online fine-tuning all network parameters to guarantee the convergence of the SAIT2NFN. Experimental results show that the proposed SAIT2NFN control system achieves the best tracking performance in comparison with type-1 NFN control systems.
Starting Page	383
Ending Page	388
File Size	555698
Page Count	6
File Format	PDF
e-ISBN	9781479923830
DOI	10.1109/CACS.2013.6734165
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-02
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Uncertainty Control systems Robustness Vectors Noise measurement Kalman filters
Content Type	Text
Resource Type	Article

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