Abstract
Linear ultrasonic motor (LUSM) has much merit, such as high precision, fast control dynamics and large driving force, etc.; however, the dynamic characteristic of LUSM is nonlinear and the precise dynamic model of LUSM is difficult to obtain. To tackle this problem, this study presents a robust neural network control (RNNC) system for LUSM to track a reference trajectory with L 2 robust tracking performance. The developed RNNC system is composed of a neural network controller and a robust controller. The neural network controller is the principal controller used to mimic an ideal controller and the robust controller is adopted to achieve L 2 robust tracking performance. The developed RNNC system is then applied to control an LUSM. Experimental results show that the developed RNNC system can achieve favorable tracking performance with unknown of LUSM model.
Similar content being viewed by others
References
Sashida T, Kenjo T (1993) An introduction to ultrasonic motors. Clarendon Press, Oxford
He S, Chen W, Tao X, Chen Z (1998) Standing wave bi-directional linearly moving ultrasonic motor. IEEE Trans Ultrason Ferroelectr Freq Control 45(5):1133–1139. doi:10.1109/58.726435
Tan KK, Lee TH, Zhou HX (2001) Micro-positioning of linear-piezoelectric motors based on a learning nonlinear PID controller. IEEE/ASME Trans Mechatoron 6(4):428–436
Wai RJ, Lin CM, Peng YF (2004) Adaptive hybrid control for linear piezoelectric ceramic motor drive using diagonal recurrent CMAC network. IEEE Trans Neural Netw 15(6):1491–1506. doi:10.1109/TNN.2004.837784
Lin CM, Peng YF (2004) Adaptive CMAC-based supervisory control for uncertain nonlinear systems. IEEE Trans Syst Man Cybern B 34(2):1248–1260. doi:10.1109/TSMCB.2003.822281
Xinghuo Y, Efe MO, Kaynak O (2002) A general backpropagation algorithm for feedforward neural networks learning. IEEE Trans Neural Netw 13(1):251–254. doi:10.1109/72.977323
Lin CM, Hsu CF (2002) Neural-network-based adaptive control for induction servomotor drive system. IEEE Trans Ind Electron 49(1):115–123. doi:10.1109/41.982255
Duarte-Mermoud MA, Suarez AM, Bassi DF (2005) Multivariable predictive control of a pressurized tank using neural networks. Neural Comput Appl 15(1):18–25. doi:10.1007/s00521-005-0003-0
Yu DL, Yu DW (2007) A new structure adaptation algorithm for RBF networks and its application. Neural Comput Appl 16(1):91–100. doi:10.1007/s00521-006-0067-5
Hsu CF (2007) Self-organizing adaptive fuzzy neural control for a class of nonlinear systems. IEEE Trans Neural Netw 18(4):1232–1241. doi:10.1109/TNN.2007.899178
Lee CH, Teng CC (2000) Indentification and control of dynamic systems using recurrent fuzzy neural networks. IEEE Trans Fuzzy Syst 8(4):349–366. doi:10.1109/91.868943
Lin CM, Hsu CF (2002) Recurrent neural network adaptive control of wing rock motion. J Guid Dyn Contr 25(6):1163–1165. doi:10.2514/2.4998
Mastorocostas PA, Theocharis JB (2002) A recurrent fuzzy-neural model for dynamic system identification. IEEE Trans Syst Man Cybern B 32(2):176–190. doi:10.1109/3477.990874
Lin CM, Hsu CF (2003) Neural network hybrid control for antilock braking systems. IEEE Trans Neural Netw 14(2):351–359. doi:10.1109/TNN.2002.806950
Lin CM, Hsu CF (2004) Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings. IEEE Trans Fuzzy Syst 12(5):733–742. doi:10.1109/TFUZZ.2004.834803
Slotine JJE, Li WP (1991) Applied nonlinear control. Prentice-Hall, Englewood Cliffs
Chen BS, Lee CH (1996) H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach. IEEE Trans Fuzzy Syst 4(1):32–43. doi:10.1109/91.481843
Acknowledgments
The authors appreciate the financial support in part from National Science Council of the Republic of China under Grant NSC93-2213-E-155-038. The authors would like to express their gratitude to the Reviewers for their valuable comments.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lin, CM., Leng, CH., Hsu, CF. et al. Robust neural network control system design for linear ultrasonic motor. Neural Comput & Applic 18, 567–575 (2009). https://doi.org/10.1007/s00521-008-0228-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-008-0228-9