DocumentCode :
976662
Title :
FPGA-based adaptive backstepping control system using RBFN for linear induction motor drive
Author :
Lin, Faa-Jeng ; Teng, L.-T. ; Chen, Ching-Yi ; Hung, Ying-Chih
Author_Institution :
Dept. of Electr. Eng., Nat. Central Univ., Chungli
Volume :
2
Issue :
6
fYear :
2008
fDate :
11/1/2008 12:00:00 AM
Firstpage :
325
Lastpage :
340
Abstract :
A field-programmable gate array (FPGA)-based adaptive backstepping control system with radial basis function network (RBFN) observer is proposed to control the mover position of a linear induction motor (LIM). First, the indirect field-oriented mechanism is adopted for controlling the LIM. Next, a backstepping control law is designed step by step for the tracking control of periodic reference trajectories, in which the uncertainties are lumped by a conservative constant. However, the lumped uncertainty is unknown and difficult to obtain in advance in practical applications. Therefore an RBFN is derived to observe the lumped uncertainty in real-time, and an adaptive backstepping control system with RBFN observer is resulted. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost, high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some simulated and experimental results. By using the adaptive backstepping control system with RBFN observer, the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.
Keywords :
adaptive control; field programmable gate arrays; induction motor drives; linear induction motors; machine vector control; neurocontrollers; observers; position control; radial basis function networks; FPGA; RBFN observer; adaptive backstepping control; field-programmable gate array; indirect field-oriented control; linear induction motor drive; lumped uncertainty; mover position control; periodic reference trajectory tracking; radial basis function network; transient control performance;
fLanguage :
English
Journal_Title :
Electric Power Applications, IET
Publisher :
iet
ISSN :
1751-8660
Type :
jour
DOI :
10.1049/iet-epa:20070178
Filename :
4665387
Link To Document :
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