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Online Speed Controller Scheme Using Adaptive Supervisory TSK ‐fuzzy CMAC for Vector Controlled Induction Motor Drive
Author(s) -
Wang S. Y.,
Tseng C. L.,
Chiu C. J.
Publication year - 2015
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.798
Subject(s) - control theory (sociology) , supervisory control , controller (irrigation) , cerebellar model articulation controller , convergence (economics) , fuzzy logic , adaptive control , scheme (mathematics) , computer science , induction motor , engineering , control engineering , control (management) , artificial intelligence , mathematics , voltage , mathematical analysis , agronomy , economics , biology , economic growth , electrical engineering
This study proposes and implements a novel adaptive supervisory Takagi–Sugeno–Kang fuzzy cerebellar model articulation controller (adaptive supervisory TSK‐FCMAC ) in a sensorless vector control for induction motor drives. The proposed adaptive supervisory TSK‐FCMAC includes a supervisory controller and an adaptive TSK‐FCMAC . The later controller incorporates the TSK ‐fuzzy control with an adaptive CMAC to perform the desired control action. Adaptive supervisory TSK‐FCMAC is derived using the Lyapunov approach and guarantees the learning error convergence. This study experimentally investigates three intelligent control schemes (adaptive supervisory TSK‐FCMAC , adaptive TSK‐FCMAC , and adaptive CMAC ) and uses the root mean square error performance index to evaluate each scheme. Results show that the proposed adaptive supervisory TSK‐FCMAC provides substantially improved performance over other schemes.