Abstract
The paper presents a study on the adaptive control of large space structures, based on the use of real time recurrent neural networks. The controller relies on two interconnected neural networks. One is used for system identification and works in parallel with the real structure. The other performs the actual control task, and feeds back onto the structure and the identification net. The two networks are trained on line, with their synaptic weights adapting to time varying system configurations. A series of numerical examples on the model of a large structure provides the basis for understanding the performances and robustness of the method proposed, and indicates the feasibility of real on line applications.
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References
Hornik, K., Stinchcombe, M. and White, H., “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, pp. 359–366, 1989.
Funahashi, K., “On the approximate realization of continuous mappings by neural networks,” Neural Networks, vol. 2, pp. 183–192, 1989.
Pao, Y., Phillips, S. M. and Sobajic, D. J., “Neural-net computing and the intelligent control of systems,” Int. J. Control, vol. 56, pp. 263–289, 1992.
Chen, S. and Billings, S. A., “Recursive prediction error parameter estimator for non-linear models,” Int. J. Control, vol. 49, pp. 569–594, 1989.
Hunt, K. J., Sbarbaro, D., Zbikowski, R. and Gawthrop, P. J., “Neural networks for control systems: a survey,” Automatica, vol. 28, pp. 1083–1112, 1992.
Haykin, S., Neural Networks: A Comprehensive Foundation, Maxwell Macmillan International, 1994.
Levin, A. U. and Narendra, K. S., “Control of nonlinear dynamical systems using neural networks—Part II: Observability, identification, and control,” IEEE Transactions on Neural Networks, vol. 7, pp. 30–42, 1996.
Morelli, G., “Applications of neural networks to the adaptive control of spacecraft,” Estec Working Paper no. 1651, 1992.
Battini, F., Apolloni, B. and Lucisano, C., “Adaptive control for AOCS/GNC: a neural network approach,” in Proceedings of the ESA Workshop on Spacecraft Guidance Navigation and Control Systems, ESA WPP-041, pp. 2B.1.1–2B.1.26, 1992.
Yen, G. G., “Identification and control of large structures using neural networks,” Computers & Structures, vol. 52, pp. 859–870, 1994.
Ercoli-Finzi A., Gallieni D. and Ricci S., “Design, modal testing and updating of a large space structure laboratory model (TESS),” in Proceedings of the 9th VPI&SU Symposium on Dynamics and Control of Large Structures, Blacksburg-Virginia, pp. 409–420, 1993.
Fabri, S. and Kadirkamanathan, V., “Dynamic structure neural networks for stable adaptive control of nonlinear systems,” IEEE Transactions on Neural Networks, vol. 7, pp. 1151–1166, 1996.
Williams, R. J. and Peng, J., “An efficient gradient based algorithm for on-line training of recurrent network trajectories,” Neural Computation, vol. 2, pp. 490–501, 1990.
Williams, R. J. and Zisper, D., “Alearning algorithm for continually running fully recurrent neural networks,” Neural Computation, vol. 1, pp. 270–280, 1989.
Guyon, I., “Applications of neural networks to character recognition,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 5, pp. 353–382, 1991.
Shieh, L. S., Wang, W. M. and Sunkel, J. W., “Design of PAM and PWM controllers for sampled-data interval systems,” Journal of Dynamic System Measurement and Control, vol. 118, pp. 673–682, 1996.
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Bernelli-Zazzera, F., Lo-Rizzo, V. Adaptive Control of Space Structures via Recurrent Neural Networks. Dynamics and Control 9, 5–20 (1999). https://doi.org/10.1023/A:1008323023512
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DOI: https://doi.org/10.1023/A:1008323023512