TY - JOUR
T1 - Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network
AU - Lin, Faa Jeng
AU - Chou, Po Huan
N1 - Funding Information:
Manuscript received March 6, 2008; revised May 28, 2008. First published November 17, 2008; current version published December 30, 2008. This work was supported by the National Science Council of Taiwan under Grant NSC 95-2221-E-008-177-MY3. F.-J. Lin is with the Department of Electrical Engineering, National Central University, Chungli 320, Taiwan (e-mail: [email protected]). P.-H. Chou is with the Department of Electrical Engineering, National Dong Hwa University, Hualien 974, Taiwan. Digital Object Identifier 10.1109/TIE.2008.927225
PY - 2009/1
Y1 - 2009/1
N2 - An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system.
AB - An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system.
KW - Lyapunov stability theorem
KW - permanent-magnet linear synchronous motors (PMLSMs)
KW - two-axis motion control system
KW - type-2 fuzzy logic system (FLS)
KW - type-2 fuzzy neural network (T2FNN)
UR - http://www.scopus.com/inward/record.url?scp=85008048477&partnerID=8YFLogxK
U2 - 10.1109/TIE.2008.927225
DO - 10.1109/TIE.2008.927225
M3 - 期刊論文
AN - SCOPUS:85008048477
SN - 0278-0046
VL - 56
SP - 178
EP - 193
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
IS - 1
ER -