TY - JOUR
T1 - Hybrid control using recurrent fuzzy neural network for linear-induction motor servo drive
AU - Lin, Faa Jeng
AU - Wai, Rong Jong
N1 - Funding Information:
Manuscript received November 11, 1999; revised September 27, 2000. This work was supported by the National Science Council of Taiwan, R.O.C. through Grant NSC 89-2213-E-033-047. F.-J. Lin is with the Department of Electrical Engineering, Chung Yuan Christian University, Chung Li 320, Taiwan. R.-J. Wai is with the Department of Electrical Engineering, Yuan Ze University, Chung Li 320, Taiwan. Publisher Item Identifier S 1063-6706(01)01363-7.
PY - 2001/2
Y1 - 2001/2
N2 - In this paper, a hybrid control system using a recurrent-fuzzy-neural network (RFNN) is proposed to control a linear-induction motor (LIM) servo drive. First, the feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, a hybrid control system is proposed to control the mover of the LIM for periodic motion. In the hybrid control system, the RFNN controller is the main tracking controller, which is used to mimic a perfect control law, and the compensated controller is proposed to compensate the difference between the perfect control law and the RFNN controller. Moreover, an on-line parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system.
AB - In this paper, a hybrid control system using a recurrent-fuzzy-neural network (RFNN) is proposed to control a linear-induction motor (LIM) servo drive. First, the feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, a hybrid control system is proposed to control the mover of the LIM for periodic motion. In the hybrid control system, the RFNN controller is the main tracking controller, which is used to mimic a perfect control law, and the compensated controller is proposed to compensate the difference between the perfect control law and the RFNN controller. Moreover, an on-line parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system.
KW - Feedback linearization
KW - Hybrid control
KW - Linear-induction motor (LIM)
KW - Recurrent-fuzzy-neural network (RFNN)
KW - Sliding mode control
UR - http://www.scopus.com/inward/record.url?scp=0035245365&partnerID=8YFLogxK
U2 - 10.1109/91.917118
DO - 10.1109/91.917118
M3 - 期刊論文
AN - SCOPUS:0035245365
SN - 1063-6706
VL - 9
SP - 102
EP - 115
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
IS - 1
ER -