Adaptive hysteresis compensation on an experimental nanopositioning platform

References

• Ahmad, I., Voda, Besançon, G., & Buche, G. (2012). Robust digital control approach for high performance tunneling current measurement system. Control Engineering Practice, 20(7), 643–653.
   Web of Science ®Google Scholar
• Aljanaideh, O., Habineza, D., Rakotondrabe, M., & Al Janaideh, M. (2015). Experimental comparison of rate-dependent hysteresis models in characterizing and compensating hysteresis of piezoelectric tube actuators. Physica B, 486(2016), 64–68.
   Google Scholar
• Barndorff-Nielsen, O. (1978). Information and exponential families in statistical theory. New York, NY: Wiley.
   Google Scholar
• Bhotto, M.Z.A., & Antoniou, A. (2011). Robust recursive least-squares adaptive-filtering algorithm for impulsive-noise environments. IEEE Signal Processing Letters, 18(3), 185–188.
   Web of Science ®Google Scholar
• Bhotto, M.Z.A., & Antoniou, A. (2013). New improved recursive least-squares adaptive-filtering algorithms. IEEE Transactions on Circuits and Systems I: Regular Papers, 60(6), 1548–1558.
   Web of Science ®Google Scholar
• Binning, G., Quate, C.F., & Rohrer, H. (1986). Atomic force microscope. Physical Review Letters, 56(9), 930–933.
   PubMed Web of Science ®Google Scholar
• Binning, G., & Rohrer, H. (1986). Scanning tunneling microscopy. IBM Journal of Research and Development, 30(4), 355–369.
   Web of Science ®Google Scholar
• Chuang, N., Petersen, I.R., & Pota, H.R. (2011, June). Robust H∞ control in fast atomic force microscopy. 2011 American control conference (ACC 2011) (pp. 2258–2265), San Francisco, CA.
   Google Scholar
• DeGroot, M.H. (2004). Optimal statistical decisions. New Jersey, NJ: John Wiley and Sons.
   Google Scholar
• Devasia, S., Eleftheriou, E., & Moheimani, S.O.R. (2007). A survey of control issues in nanopositioning. IEEE Transactions on Control Systems Technology, 15(5), 802–823.
   Web of Science ®Google Scholar
• Dokoupil, J., Papež, M., & Václavek, P. (2015). Comparison of Kalman filters formulated as the statistics of the Normal-inverse-Wishart distribution. 54th Annual conference on decision and control (CDC 2015) (pp. 5008–5013), Osaka, Japan.
   Google Scholar
• Eielsen, A.A., Gravdahl, J.T., & Pettersen, K.Y. (2012). Adaptive feed-forward hysteresis compensation for piezoelectric actuators. Review of Scientific Instruments, 83, 085001(1–8).
   PubMed Web of Science ®Google Scholar
• Fortescue, T.R., Kershenbaum, L.S., & Ydstie, B.E. (1981). Implementation of self-tuning regulators with variable forgetting factors. Automatica, 17(6), 831–835.
   Web of Science ®Google Scholar
• Gu, G.-Y., Zhu, L.-M., Su, C.-Y., Ding, H., & Fatikow, S. (2014). Modeling and control of piezo-actuated nanopositioning stages: A survey. IEEE Transactions on Automation Science and Engineering, 13(1), 313–332.
   Web of Science ®Google Scholar
• Habineza, D., Rakotondrabe, M., & Le Gorrec, Y. (2015). Bouc-Wen modeling and feedforward control of multivariable hysteresis in piezoelectric systems: Application to a 3-DoF piezotube scanner. IEEE Transactions on Control Systems Technology, 23(5), 1797–1806.
   Web of Science ®Google Scholar
• Hughes, D., & Wen, J.T. (1997). Preisach modeling of piezoceramic and shape memory alloy hysteresis. Smart Materials and Structures, 6(3), 287–300.
   Web of Science ®Google Scholar
• Janaideh, M.A., Mao, J., Rakheja, S., Xie, W., & Su, C.-Y. (2008, December). Generalized Prandtl-Ishlinskii hysteresis model: Hysteresis modeling and its inverse for compensation in smart actuators. 47th IEEE conference on decision and control (CDC 2008) (pp. 5182–5187), Cancun, Mexico.
   Google Scholar
• Janaideh, O.A., Janaideh, M. Al, & Rakotondrabe, M. (2015, May). Inversion-free feedforward dynamic compensation of hysteresis nonlinearities in piezoelectric micro/nano-positioning actuators. 2015 IEEE international conference on robotics and automation ( ICRA 2015, pp. 2673–2678), Seattle, WA.
   Google Scholar
• Kárný, M., Böhm, J., Guy, T.V., Jirsa, L., Nagy, I., & Nedoma, P. (2006). Optimized Bayesian dynamic advising. London: Springer.
   Google Scholar
• Kuhnen, K. (2003). Modeling, identification and compensation of complex hysteretic nonlinearities: A modified Prandtl-Ishlinskii approach. European Journal of Control, 9(4), 407–418.
   Web of Science ®Google Scholar
• Kuhnen, K., & Janocha, H. (2001). Inverse feedforward controller for complex hysteretic nonlinearities in smart-materials systems. Control and Intelligent Systems, 29(3), 74–83.
   Google Scholar
• Kullback, S., & Leibler, R.A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1), 79–86.
   Google Scholar
• Landau, I.D., Lozano, R., M’Saad, M., & Karimi, A. (2011). Adaptive control: Algorithms, analysis and applications. London: Springer-Verlag.
   Google Scholar
• Leang, K.K., & Devasia, S. (2007). Feedback-linearized inverse feedforward for creep, hysteresis, and vibration compensation in AFM piezoactuators. IEEE Transactions on Control Systems Technology, 15(5), 927–935.
   Web of Science ®Google Scholar
• Ousaid, A.M., Grossard, M., & Boukallel, M. (2010, September). Adaptive control based on an Extended Kalman filter for Hammerstein system – application to piezoelectric actuators. 5th IFAC sysmposium on mechatronic systems (pp. 311–316). Cambridge, MA.
   Google Scholar
• Peterka, V. (1981). Bayesian system identification. Automatica, 17(1), 41–53.
   Web of Science ®Google Scholar
• Ru, Ch., Chen, L., Shao, B., Rong, W., & Sun, L. (2009). A hysteresis compensation method of piezoelectric actuator: Model, identification and control. Control Engineering Practice, 17(9), 1107–1114.
   Web of Science ®Google Scholar
• Ryba, Ł., Dokoupil, J., Voda, A., & Besançon, G. (2015). A real-time inverse-based hysteresis compensation with adaptation. 54th Annual conference on decision and control (pp. 7335–7340), Osaka, Japan.
   Google Scholar
• Ryba, Ł., Voda, A., & Besançon, G. (2014). 3DOF nanopositioning control of an experimental tunneling current-based platform. IEEE conference on control applications (CCA) (part of MSC 2014) (pp. 1976–1981), Nice/Antibes, France.
   Google Scholar
• Ryba, Ł., Voda, A., & Besançon, G. (2015). Experimental comparison of disturbance observer and inverse-based hysteresis compensation in 3D nanopositioning piezoactuation. Sensors and Actuators A: Physical, 236, 190–205.
   Web of Science ®Google Scholar
• Shan, Y., & Leang, K.K. (2012). Accounting for hysteresis in repetitive control design: Nanopositioning example. Automatica, 48(8), 1751–1758.
   Web of Science ®Google Scholar
• Södeström, T.S., & Stoica, P.G. (1989). System identification. Cambridge, MA: Prentice Hall.
   Google Scholar
• Xu, Q. (2013). Identification and compensation of piezoelectric hysteresis without modeling hysteresis inverse. IEEE Transactions on Industrial Electronics, 60(9), 3927–3937.
   Web of Science ®Google Scholar
• Xu, Q. (2014). Digital sliding-mode control of piezoelectric micropositioning system based on input-output model. IEEE Transactions on Industrial Electronics, 61(10), 5517–5526.
   Web of Science ®Google Scholar
• Yanding, Q., Yanling, T., Dawei, Z., Shirinzadeh, B., & Fatikow, S. (2013). A novel direct inverse modeling approach for hysteresis compensation of piezoelectric actuator in feedforward applications. IEEE/ASME Transactions on Mechatronics, 18(3), 981–989.
   Web of Science ®Google Scholar
• Ydstie, B.E., & Sargent, R.W.H. (1986). Convergence and stability properties of an adaptive regulator with variable forgetting factor. Automatica, 22(6), 749–751.
   Web of Science ®Google Scholar
• Yi, J., Chang, S., & Shen, Y. (2009). Disturbance-observer-based hysteresis compensation for piezoelectric actuators. IEEE/ASME Transactions on Mechatronics, 14(4), 456–464.
   Web of Science ®Google Scholar