DOI QR코드

DOI QR Code

Tracking control of variable stiffness hysteretic-systems using linear-parameter-varying gain-scheduled controller

  • Pasala, D.T.R. (Department of Civil and Environmental Engineering, Rice University) ;
  • Nagarajaiah, S. (Department of Civil and Envi. Engrg. and Mechanical Engrg. and Mat. Sci., Rice University) ;
  • Grigoriadis, K.M. (Department of Mechanical Engineering, University of Houston)
  • Received : 2011.08.04
  • Accepted : 2012.03.20
  • Published : 2012.04.25

Abstract

Tracking control of systems with variable stiffness hysteresis using a gain-scheduled (GS) controller is developed in this paper. Variable stiffness hysteretic system is represented as quasi linear parameter dependent system with known bounds on parameters. Assuming that the parameters can be measured or estimated in real-time, a GS controller that ensures the performance and the stability of the closed-loop system over the entire range of parameter variation is designed. The proposed method is implemented on a spring-mass system which consists of a semi-active independently variable stiffness (SAIVS) device that exhibits hysteresis and precisely controllable stiffness change in real-time. The SAIVS system with variable stiffness hysteresis is represented as quasi linear parameter varying (LPV) system with two parameters: linear time-varying stiffness (parameter with slow variation rate) and stiffness of the friction-hysteresis (parameter with high variation rate). The proposed LPV-GS controller can accommodate both slow and fast varying parameter, which was not possible with the controllers proposed in the prior studies. Effectiveness of the proposed controller is demonstrated by comparing the results with a fixed robust $\mathcal{H}_{\infty}$ controller that assumes the parameter variation as an uncertainty. Superior performance of the LPV-GS over the robust $\mathcal{H}_{\infty}$ controller is demonstrated for varying stiffness hysteresis of SAIVS device and for different ranges of tracking displacements. The LPV-GS controller is capable of adapting to any parameter changes whereas the $\mathcal{H}_{\infty}$ controller is effective only when the system parameters are in the vicinity of the nominal plant parameters for which the controller is designed. The robust $\mathcal{H}_{\infty}$ controller becomes unstable under large parameter variations but the LPV-GS will ensure stability and guarantee the desired closed-loop performance.

Keywords

References

  1. Apkarian, P. and Adams, R.J. (1998), "Advanced gain-scheduling techniques for uncertain systems", IEEE T. Autom., 6(1), 21-32.
  2. Apkarian, P. and Gahinet, P. (1995), "A convex characterization of gain-scheduled controllers", IEEE T. Autom., 40(5), 853-864. https://doi.org/10.1109/9.384219
  3. Apkarian, P., Nemirovski, A., Laub, A. and Chilali, M. (1995), LMI Control Toolbox User's Guide, The MathWorks Inc.: Natick.
  4. Bai, Y. (2006), Advanced Controls of Large Scale Structural Systems Using Linear Matrix Inequality Methods, Ph.D. Thesis, University of Houston, Houston.
  5. Becker, G.S. (1993), Quadratic Stability and Performance of Linear Parameter Dependent Systems, Ph.D. Thesis,Univ. of California, Berkley.
  6. Bouc, R. (1967), "Forced vibration of mechanical systems with hysteresis", Proceedings of the 4th Conf. on Nonlin., Oscill.
  7. Doyle, J.C., Glover, K., Khargonekar, P.P. and Francis, B.A. (1989), "State-space solutions to standard H2 and H control problems", IEEE T. Autom., 34, 831-847. https://doi.org/10.1109/9.29425
  8. Ganley, T., Hung, D.L.S., Zhu, G. and Tan, X. (2011), "Modeling and inverse compensation of temperaturedependent ionic polymer-metal composite sensor dynamics", IEEE/ASME T. Mechatronics, 16(1), 80-89. https://doi.org/10.1109/TMECH.2010.2090665
  9. Iyer, R. and Tan, X. (2009), "Control of hysteretic systems through inverse compensation: Inversion algorithms, adaptation, and embedded implementation", IEEE Contr. Syst. Mag., 29(1), 83-99.
  10. Krejci, P. and Kuhnen, K. (2001), "Inverse control of systems with hysteresis and creep", IEEE Proc. Contr. Theory Appl., 148(3), 185-192. https://doi.org/10.1049/ip-cta:20010375
  11. Liu, S.C. (2008) "Sensors, smart structures technology and steel structures", Smart Struct. Syst., 4(5), 525-531.
  12. Mehendale, C.S. (2005), Advanced Gain-scheduling for Nonlinear Control Design Using Linear Parameter Varying Systems Theory, Ph.D. Thesis, Univ. of Houston, Houston
  13. Mehendale, C.S., Fialho, I.J. and Grigoriadis, K.M. (2003), "Adaptive active microgravity isolation using LPV gain-scheduling methods", Proceedings of the American Control Conference, Denver, USA.
  14. Mehendale, C.S. and Grigoriadis, K.M. (2004), "Hysteresis compensation using LPV gain-scheduling", Am. Control Conf., 2, 1380-1385.
  15. Mate, D. (1998), Experimental and analytical study of semiactive variable stiffness control system, MS Thesis, University of Missouri, Columbia.
  16. Nagarajaiah, S. and Mate, D. (1998), "Semi-active control of continuously variable stiffness system", Proceedings of the 2nd World Conference on Structural Control, Tokyo, Japan.
  17. Nagarajaiah, S. and Sahasrabudhe, S. (2006), "Seismic response control of smart sliding isolated buildings using variable stiffness systems: An experimental and numerical study", Earthq. Eng. Struct. D., 35(2), 177-197. https://doi.org/10.1002/eqe.514
  18. Nagarajaiah, S. and Varadarajan, N. (2005), "Semi-active control of wind excited building with variable stiffness TMD using short time Fourier transform", Eng. Struct., 27, 431-441. https://doi.org/10.1016/j.engstruct.2004.10.015
  19. Oloomi, H. and Shafai, B. (2003), "Weight selection in mixed sensitivity robust control for improving the sinusoidal tracking performance", Proceedings of the 42nd IEEE Conference on Decision and Control.
  20. Pasala, D.T.R., Nagarajaiah, S. and Grigoriadis, K.M. (2008), "Response control of SAIVS system using LPV gain-scheduling controller", Proceedings of the Earth and Space Conference, Long Beach California, USA.
  21. Pasala, D.T.R., Nagarajaiah, S. and Grigoriadis, K.M. (2009), "Gain scheduled control of hysteretic systems", Proceedings of the SPIE, Smart Structure/NDE, San Diego, USA.
  22. Rugh, W.J. and Shamma, J.S. (2000), "Research on gain scheduling", Automatica, 36(10), 1401-1425. https://doi.org/10.1016/S0005-1098(00)00058-3
  23. Salapaka, S., Sebastian, A., Cleveland, J.P. and Salapaka, M.V. (2002), "High bandwidth nano-positioner: A robust control approach", Rev. Sci. Instrum., 73(9), 3232-3241. https://doi.org/10.1063/1.1499533
  24. Skelton, R.E., Iwasaki, T. and Grigoriadis, K. (1998), A Unified Algebraic Approach to Linear Control Design, Taylor & Francis, Great Britain.
  25. Sivaselvan, M.V. and Reinhorn, A.M. (2000), "Hysteretic Models for Deteriorating Inelastic Structures", J. Eng. Mech.- ASCE, 126(6), 633-640. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:6(633)
  26. Skogestad, S. and Postlethwaite, I. (1996), Multivariable Feedback Control: Analysis and Design, John Wiley & Sons, New York.
  27. Smith, R.C. (2001), "Inverse compensation for hysteresis in magnetostrictive transducers", Math. Comput. Model., 33(1-3), 285-298. https://doi.org/10.1016/S0895-7177(00)00245-4
  28. Song, G., Zhao, J., Zhou, X. and Garcia, J. (2005), "Tracking control of a piezoceramic actuator with hysteresis compensation using inverse Preisach model", IEEE T. Mechatronics, 10(2), 198-209. https://doi.org/10.1109/TMECH.2005.844708
  29. Song, G., Ma, N. and Lee, H.J. (2007), "Position estimation and control of SMA actuators based on electrical resistance measurement", Smart Struct. Syst., 3(2), 189-200. https://doi.org/10.12989/sss.2007.3.2.189
  30. Spencer, B.F. and Nagarajaiah, S. (2003), "State of the art of structural control", J. Struct. Eng. - ASCE, 129(7), 845-856. https://doi.org/10.1061/(ASCE)0733-9445(2003)129:7(845)
  31. Tan, X. and Baras, J.S. (2003), Adaptive Identification and Control of Hysteresis in Smart Materials, Tech. Rep. CDCSS TR 2003-3 (ISR TR 2003-40): Univ. of Maryland.
  32. Taware, A. and Tao, G. (2003), Control of Sandwich Nonlinear Systems, Springer-verlag, Heidelberg.
  33. Wen, Y.K. (1976), "Method for random vibration of hysteretic systems", J. Eng. Mech.- ASCE, 102(2), 249-263.
  34. Wu, F., Yang, X.H., Packard, A. and Becker, G. (1997), "Induced $L_2$-norm control for LPV systems with bounded parameter variation rates", Int. J. Nonlinear Robust control.
  35. Wu, F. (1995), Control of Linear Parameter Varying Systems, Ph.D. Thesis Univ. of California, Berkley.
  36. Wu, F., Yang, X.H., Packard, A. and Becker, G. (1995), "Induced $L_2$-norm control for LPV system with bounded parameter variation rates", Proceedings of the American Control Conference.
  37. Zhang, F., Grigoriadis, K.M. and Fialho, I.J. (2009), "Linear parameter-varying control for active vibration isolation systems with stiffness hysteresis", J. Vib. Control, 15, 527-547. https://doi.org/10.1177/1077546308096105
  38. Zheng, Q. and Wu, F. (2009), "Stabilization of polynomial nonlinear systems using rational Lyapunov functions", Int. J. Control, 82(9), 1605-1615. https://doi.org/10.1080/00207170802627267
  39. Zapaterio, M., Luo, N., Taylor, E. and Dyke, S.J. (2010), "Modeling and identification of a class of MR fluid foam dampers", Smart Struct. Syst., 6(2), 101-113. https://doi.org/10.12989/sss.2010.6.2.101

Cited by

  1. Adaptive-length pendulum smart tuned mass damper using shape-memory-alloy wire for tuning period in real time vol.13, pp.2, 2014, https://doi.org/10.12989/sss.2014.13.2.203
  2. Determining the physical limits on semi-active control performance: a tutorial vol.21, pp.5, 2014, https://doi.org/10.1002/stc.1602
  3. A Computationally Efficient Algorithm for Real-Time Tracking the Abrupt Stiffness Degradations of Structural Elements vol.31, pp.6, 2016, https://doi.org/10.1111/mice.12217