Summary
In this chapter, we will discuss two important areas of applications of artificial neural networks, i.e., system identification and adaptive control. The neural network based approach has some significant advantages over conventional methods, such as adaptive learning ability, distributed associability, as well as nonlinear mapping ability. In addition, it does not require a priori knowledge on the model of the unknown system, so it is more flexible to implement in practice.
This chapter contains three sections. Section 1 gives a general introduction on system identification and adaptive control. Section 2 focuses on the application of artificial neural networks for rotorcraft acoustic data modeling and prediction. In Sect. 3, a neural network controller is developed for a DC voltage regulator.
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References
Carrasco JM, Galván E, Valderrama GE, Ortega R, Stankovic A (2000) Analysis and experimentation of nonlinear adaptive controllers for the series resonant converter. IEEE Trans Power Electron 15(3): 536–544
Chetty PR (1992) Resonant power supplies: Their history and status. IEEE Aerospace and Electronics System Magazine 7(4): 23–29
Choi HS, Cho BH (2002) Novel zero-current-switching (ZCS) PWM switch cell minimizing additional conduction loss. IEEE Trans on Industrial Electronics 49(1): 165–172
Choi B, Kim J, Cho BH, Choi S, Wildrick CM (2002) Designing control loop for DC-to-DC converters loaded with unknown AC dynamics. IEEE Trans on Industrial Electronics 49(4): 925–932
Choi HS, Kim JW, Cho BH (2002) Novel zero-voltage and zero-current-switching (ZVZCS) full-bridge PWM converter using coupled output inductor. IEEE Trans on Power Electronics 17(5): 641–648
El-Sharkh MY, Rahman A, Alam MS (2004) Neural networks-based control of active and reactive power of a stand-alone PEM fuel cell power plant. Journal of Power Resources 135 (1–2): 88–94
Fu J (2004) Modeling and analysis of rotorcraft acoustic data with neural networks. Thesis, California Polytechnic State University, San Luis Obispo
Fu J, Yu XH (2006) Rotorcraft Acoustic Noise Estimation and Outlier Detection. In: Proceedings of the IEEE World Congress on Computational Intelligence (International Joint Conference on Neural Networks). pp. 8834–8838
Holger G et al. (2003) Neural networks for BVI system identification. In: Proceedings of the 29th European Rotorcraft Forum, Germany
Iglewicz B, Hoaglin DC (1993) How to detect and handle outliers. ASQC Quality Press, Wisconsin
Johnson W (1980) Helicopter theory. Princeton University Press, New Jersey
Kamran F, Harley RG, Burton B, Habetler TG, Brooke MA (1998) A fast on-line neural-network training algorithm for a rectifier regulator. IEEE Trans on Power Electronics 13(2): 366–371
Kim MG, Youn MJ (1991) An energy feedback control of series resonant converters. IEEE Trans Power Electron 6(4): 338–345
Kitaplioglu C. Blade-Vortex interaction noise of a full-scale XV-15 rotor tested in the NASA Ames 80- by 120-foot wind tunnel, NASA technical memorandum 208789. National Aeronautics and Space Administration, Moffett Field
Li W (2006) A neural network controller for a class of phase-shifted full-bridge DC–DC converter. Thesis, California Polytechnic State University, San Luis Obispo
Li W, Yu XH (2007) Improving DC power supply efficiency with neural network controller. In: Proceedings of the IEEE International Conference on Control and Automation. pp. 1575–1580
Li W, Yu XH (2007) A self-tuning controller for real-time voltage regulation. In: Proceedings of the IEEE International Joint Conference on Neural Networks. pp. 2010–2014
Lin F, Ye H (1999) Switched inductor two-quadrant DC–DC converter with neural network control. In: IEEE International Conference on Power Electronics and Drive Systems. pp. 1114–1119
Lo C, Schmitz F (2000) Model-Based Neural Networks for Rotorcraft Ground Noise Prediction. In: Proceedings of the 38th AIAA Aerospace Sciences Meeting & Exhibit, Nevada
Quero JM, Carrasco JM, and Franquelo LG (2002) Implementation of a neural controller for the series resonant converter. IEEE Trans on Industrial Electronics 49(3): 628–639
Ruan X, Yan Y (2001) A novel zero-voltage and zero-current-switching PWM full-bridge converter using two diodes in series with the lagging leg. IEEE Trans on Industrial Electronics 48(4): 777–785
Schalkoff RJ (1997) Artificial neural networks. McGraw-Hill, New York
Texas Instrument Inc (2001) BiCMOS advanced phase shift PWM controller, Data sheet of UCC3895. Texas Instrument, TX
Yu XH, Fu J (2004) Aeroacoustic data analysis with artificial neural networks. In: Proceedings of the IASTED International Conference on Intelligent Systems and Control. pp. 244–247
Zaknich A (2003) Neural Network for Intelligent Signal Processing. World Scientific, Singapore
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Yu, XH. (2008). Applications of Neural Networks to Dynamical System Identification and Adaptive Control. In: Liu, Y., Sun, A., Loh, H.T., Lu, W.F., Lim, EP. (eds) Advances of Computational Intelligence in Industrial Systems. Studies in Computational Intelligence, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78297-1_15
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DOI: https://doi.org/10.1007/978-3-540-78297-1_15
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