Abstract
For many nonlinear factors which appear in the tracking system of Permanent Magnet Linear Synchronous Motor (PMLSM), the two hybrid controllers based on the radial basis function neural network were proposed, the hybrid neural network controllers were composed of PID controller and RBF neural network controller in series, and their structures depended on the series orders. The two hybrid neural network controllers realized the nonlinear PID control, which possessed the ability of parameters self-tuning, simple structure and are easy to implement in the practice. The experimental results showed the feasibility and effectiveness of the two hybrid controllers.
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Hao S-H, Cai Y, Zhang W-F et al (2010) Research on high-speed positioning of AC servo system based on feedforward control. Small Spec Electr Mach 38(2):35–40
Cheng Y, Yang J, Huang Q et al (2011) Characteristics of inductance parameters and thrust linear modeling of PMLSM with combinational iron-cored primary. Int Conf Consum Electron Commun Netw 22:142–145
Panah PG, Shafiei A, Parsa Pour A, Dehkordi M et al (2011) Velocity control of a PMLSM using a brain emotional learning based intelligent control strategy. IEEE Int Conf Syst Eng Technol 32:47–52
Zhang L, Xuan-ju D, Zeng S-L et al (2010) Kind of double model control for permanent magnet linear servo system. Electr Transm 40(2):53–56
Fu Z, Li H, Wang Z et al (2009) Sliding mode control based on neural network thrust observer for PMLSM. Electr Transm 39(11):48–51
Lu H-C, Chang M-H et al (2009) Automatic generation fuzzy neural network controller with supervisory control for permanent magnet linear synchronous motor. IEEE Conf Ind Electron Appl 243:3124–3129
Ahn H-S, Chen YQ, Dou H et al (2005) State-periodic adaptive compensation of cogging and coulomb friction in permanent-magnet linear motors. IEEE Trans Magn 41(1):90–98
Chen SL, Tan KK, Huang S et al (2010) Modeling and compensation of ripples and friction in permanent-magnet linear motor using a hysteretic relay. IEEE Trans Mechatron 15(4):586–594
Nakamura Y, Morimoto K, Wakui S et al (2011) Experimental validation of control for a positioning stage by feedback error learning. Int Conf Adv Mechatron Syst 22:11–16
Li G, Zang J, ZENG A et al (2009) PID control based on BP neural network. Comput Simul 26(9):128–131
Micro-nano technology Co., LTD (2010) cSPACE rapid control prototyping system manual of micro-nano technology, 22:330–337
Shen Y, Ji Z (2005) Study on modeling and simulation of permanent-magnet synchronous-motor control system based on C MEX S—function. J Syst Simul 17(8):1820–1825
Acknowledgments
This work has been supported by the National Nature Science Foundation Project (60964001), Science Foundation Project of Guangxi Province (0991019Z) and Major Research Project of Guangxi Department of Education (201101ZD007).
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Zhao, L., Zhu, X., Yang, H., Dang, X. (2013). Design of Hybrid Controllers Based on Radial Basis Function Neural Network. In: Du, W. (eds) Informatics and Management Science V. Lecture Notes in Electrical Engineering, vol 208. Springer, London. https://doi.org/10.1007/978-1-4471-4796-1_26
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DOI: https://doi.org/10.1007/978-1-4471-4796-1_26
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