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Adaptive Stabilization of Partially Damaged Vibrating Structures

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper an online adaptive continuous-time control algorithm will be studied in the vibration control problem. The examined algorithm is a Reinforcement Learning based scheme able to adapt to the changing system’s dynamics and providing control converging to the optimal control. Firstly, a brief description of the algorithm is provided. Then, the algorithm is studied by the numeric simulation. The controlled model is a simple conjugate oscillator with sudden change of its rigidity. The effectiveness of the adaptation of the algorithm is compared to the simulation results of controlling the same object by the traditional Linear Quadratic Regulator. Because of the lack of constraints for a system size or its linearity, this algorithm is suitable for optimal stabilization of more complex vibrating structures.
Rocznik
Strony
65--82
Opis fizyczny
Bibliogr 26 poz., wykr.
Twórcy
  • Institute of Fundamental Technological Research Polish Academy of Sciences
autor
  • Institute of Fundamental Technological Research Polish Academy of Sciences
autor
  • Institute of Fundamental Technological Research Polish Academy of Sciences
Bibliografia
  • 1. Al-Tamimi, A. and Lewis, F. (2007). Discrete-time nonlinear hjb solution using approximate dynamic programming: Convergence proof. In 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, pages 38–43. IEEE.
  • 2. Baird, L. C. (1994). Reinforcement learning in continuous time: Advantage updating. In Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on, volume 4, pages 2448–2453. IEEE.
  • 3. Beard, R. W., Saridis, G. N., and Wen, J. T. (1997). Galerkin approximations of the generalized Hamilton-Jacobi-Bellman equation. Automatica, 33(12):2159–2177.
  • 4. Dyke, S., Sain, M., Carlson, J., et al. (1996). Modeling and control of magnetorheological dampers for seismic response reduction. Smart Materials and Structures, 5(5):565–575.
  • 5. Fujino, Y., Soong, T., and Spencer, B. (1996). Structural control: Basic concepts and applications. In Building an International Community of Structural Engineers, pages 1277–1287. ASCE.
  • 6. Hillsley, K. and Yurkovich, S. (1991). Vibration control of a two-link flexible robot arm. In Robotics and Automation, 1991. Proceedings., 1991 IEEE International Conference on, pages 212–216. IEEE.
  • 7. Hu, Y.-R. and Ng, A. (2005). Active robust vibration control of flexible structures. Journal of sound and vibration, 288(1):43–56.
  • 8. Kalman, R. E. et al. (1960). Contributions to the theory of optimal control.
  • 9. Kar, I., Miyakura, T., and Seto, K. (2000a). Bending and torsional vibration control of a flexible plate structure using H∞ - based robust control law. IEEE Transactions on Control Systems Technology, 8(3):545–553.
  • 10. Kar, I. N., Seto, K., and Doi, F. (2000b). Multimode vibration control of a flexible structure using H∞ -based robust control. IEEE/ASME transactions on Mechatronics, 5(1):23–31.
  • 11. Kucuk, I., Yildirim, K., Sadek, I., and Adali, S. (2013). Active control of forced vibrations in a beam via maximum principle. In Modeling, Simulation and Applied Optimization (ICMSAO), 2013 5th International Conference on, pages 1–4. IEEE.
  • 12. Li, X., Agarwal, R. K., and Shue, S.-P. (1994). Optimal control and H∞ ufilter for control of Timoshenko beam vibrations using piezoelectric material. In Decision and Control, 1998. Proceedings of the 37th IEEE Conference on, volume 2, pages 1566–1571. IEEE.
  • 13. Mohamed, Z., Chee, A., Hashim, A. M., Tokhi, M. O., Amin, S. H., and Mamat, R. (2006). Techniques for vibration control of a flexible robot manipulator. Robotica, 24(04):499–511.
  • 14. Peng, F., Ng, A., and Hu, Y.-R. (2005). Actuator placement optimization and adaptive vibration control of plate smart structures. Journal of Intelligent Material Systems and Structures, 16(3):263–271.
  • 15. Pisarski, D. (2011). Semi-active control system for trajectory optimization of a moving load on an elastic continuum.
  • 16. Pisarski, D. and Bajer, C. I. (2010). Semi-active control of 1d continuum vibrations under a travelling load. Journal of sound and vibration, 329(2):140–149.
  • 17. Sakai, C., Ohmori, H., and Sano, A. (2003). Modeling of mr damper with hysteresis for adaptive vibration control. In 42nd IEEE Conference on Decision and Control.
  • 18. Singhose, W. (2009). Command shaping for flexible systems: A review of the first 50 years. International Journal of Precision Engineering and Manufacturing, 10(4):153–168.
  • 19. Soong, T. and Constantinou, M. (1994). Passive and active structural vibration control in civil engineering.
  • 20. Spencer Jr, B. (1996). Recent trends in vibration control in the usa. In Proc., 3rd Int. Conf. on Motion and Vibr. Control, pages K1–K6.
  • 21. utton, R. S., Barto, A. G., and Williams, R. J. (1992). Reinforcement learning is direct adaptive optimal control. IEEE Control Systems, 12(2):19–22.
  • 22. Symans, M. D. and Constantinou, M. C. (1999). Semi-active control systems for seismic protection of structures: a state-of-the-art review. Engineering Structures, 21(6):469–487.
  • 23. Tzes, A. and Yurkovich, S. (1993). An adaptive input shaping control scheme for vibration suppression in slewing flexible structures. IEEE Transactions on Control Systems Technology, 1(2):114–121.
  • 24. Tzou, H. and Tseng, C. (1990). Distributed piezoelectric sensor/actuator design for dynamic measurement/control of distributed parameter systems: a piezoelectric finite element approach. Journal of sound and vibration, 138(1):17–34.
  • 25. Vrabie, D. and Lewis, F. (2009). Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems. Neural Networks, 22(3):237–246.
  • 26. Youn, S.-H., Han, J.-H., and Lee, I. (2000). Neuro-adaptive vibration control of composite beams subject to sudden delamination. Journal of sound and vibration, 238(2):215–231.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-78f56681-0156-447f-80f1-6b94d63097e3
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