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Adaptive support vector regression for UAV flight control. (English) Zbl 1217.68187

Summary: This paper explores an application of support vector regression for adaptive control of an unmanned aerial vehicle (UAV). Unlike neural networks, support vector regression (SVR) generates global solutions, because SVR basically solves quadratic programming (QP) problems. With this advantage, the input-output feedback-linearized inverse dynamic model and the compensation term for the inversion error are identified off-line, which we call I-SVR (inversion SVR) and C-SVR (compensation SVR), respectively. In order to compensate for the inversion error and the unexpected uncertainty, an online adaptation algorithm for the C-SVR is proposed. Then, the stability of the overall error dynamics is analyzed by the uniformly ultimately bounded property in the nonlinear system theory. In order to validate the effectiveness of the proposed adaptive controller, numerical simulations are performed on the UAV model.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
93C40 Adaptive control/observation systems

Software:

LIBSVM
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References:

[1] Cauwenberghs, G., & Poggio, T. (2001). Incremental and decremental support vector machine lenarning. In: Proceedings of the neural information processing systems conference; Cauwenberghs, G., & Poggio, T. (2001). Incremental and decremental support vector machine lenarning. In: Proceedings of the neural information processing systems conference
[2] Chang, C., & Lin, C. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm; Chang, C., & Lin, C. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm
[3] Cherkassky, V.; Ma, Y., Practical selection of svm parameters and noise estimation for svm regression, Neural Networks, 17, 113-126 (2004) · Zbl 1075.68632
[4] Giulietti, F.; Pollini, L.; Innocenti, M., Autonomous formation flight, IEEE Control Systems Magazine, 20, 34-44 (2000)
[5] Harmon, F. G.; Frank, A. A.; Joshi, S. S., The control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle using a cmac neural network, Neural Networks, 18, 772-780 (2005)
[6] Holzapfel, W.; Sofsky, M.; Neuschaefer-Rube, U., Road profile recognition for autonomous car navigation and navstar gps support, IEEE Transactions on Aerospace and Electronic Systems, 39, 2-12 (2003)
[7] Ioannou, P., Robust and adaptive control (1996), Prentice-Hall: Prentice-Hall NJ · Zbl 0847.93031
[8] Iplicki, S., Support vector machine-based generalized predictive control, International Journal of Robust and Nonlinear Control, 16, 843-862 (2006) · Zbl 1134.93419
[9] Khalil, H. K., Nonlinear Systems (2002), Prentice-Hall: Prentice-Hall NJ · Zbl 0626.34052
[10] Kim, B. S.; Calise, A. J., Nonlinear flight control using neural networks, Journal of Guidance, Control, and Dynamics, 20, 26-33 (1997) · Zbl 0925.93738
[11] Morelli, E. A.; Klein, V., Application of system identification to aircraft at nasa langley research center, Journal of Aircraft, 42, 12-25 (2005)
[12] Polycarpou, M. M.; Ioannou, P. A., Learning and convergence analysis of neural-type structured networks, IEEE Transactions on Neural Networks, 3, 39-50 (1992)
[13] Ryan, A., Zennaro, M., Howell, A., Sengupta, R., & Hedrick, J. K. (2004). An overview of emerging results in cooperative uav control. In: Proceeding of IEEE conf. decision and control; Ryan, A., Zennaro, M., Howell, A., Sengupta, R., & Hedrick, J. K. (2004). An overview of emerging results in cooperative uav control. In: Proceeding of IEEE conf. decision and control
[14] Sanner, R. M.; Slotine, J. J.E., Gaussian networks for direct adaptive control, IEEE Transactions on Neural Networks, 3, 837-868 (1992)
[15] Schölkopf, B., Bartlett, P., Smola, A. J., & Williamson, R. (1998). Shrinking the tube: a new support vector regression algorithm. In: Proceedings of the neural information processing systems conference; Schölkopf, B., Bartlett, P., Smola, A. J., & Williamson, R. (1998). Shrinking the tube: a new support vector regression algorithm. In: Proceedings of the neural information processing systems conference
[16] Schölkopf, B.; Burges, C. J.S.; Smola, A. J., Advances in kernel methods: support vector learning (1999), The MIT Press: The MIT Press Cambridge
[17] Shin, D.; Kim, Y., Nonlinear discrete-time reconfigurable flight control law using neural networks, IEEE Transactions on Control Systems Technology, 14, 408-422 (2006)
[18] Smola, A. J., & Schölkopf, B. (1998). A tutorial on support vector regression. NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, London.; Smola, A. J., & Schölkopf, B. (1998). A tutorial on support vector regression. NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, London.
[19] Stevens, B. L.; Lewis, F. L., Aircraft control and simulation (2003), Wiley & Sons: Wiley & Sons NJ
[20] Suykens, J. A.K.; Vandewalle, J.; Moor, B. D., Optimal control by least squares support vector machines, Neural Networks, 14, 23-35 (2001)
[21] Talebi, H. A.; Khorasani, K.; Patel, R. V., Neural network based control schemes for flexible-link manipulators: simulations and experiments, Neural Networks, 11, 1357-1377 (1998)
[22] Vapnik, V. N., The nature of statistical learning theory (1995), Springer: Springer London · Zbl 0934.62009
[23] Vapnik, V. N., Support vector method for function estimation (1998), Kluwer Academic Publishers: Kluwer Academic Publishers Boston, pp. 55-86
[24] Wang, H.; Pi, D.; Sun, Y., Online svm regression algorithm-based adaptive inverse control, Neurocomputing, 70, 952-959 (2007)
[25] Xi, X. C.; Poo, A. N.; Chou, S. K., Support vector regression model predictive control on a hvac plant, Control Engineering Practice, 15, 897-908 (2007)
[26] Yabuta, T., & Yamada, T. (1991). Learning control using neural networks. In: Proceedings of IEEE conf. robotics and automation; Yabuta, T., & Yamada, T. (1991). Learning control using neural networks. In: Proceedings of IEEE conf. robotics and automation
[27] Zhao, Q.; Principe, J. C., Support vector machines for sar automatic target recognition, IEEE Transactions on Aerospace and Electronic Systems, 37, 643-654 (2001)
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