A hybrid model for rate-dependent hysteresis in piezoelectric actuators

Sensors and Actuators, A: Physical

Volumn 157, Issue 1, 2010, Pages 54-60

  Zhang, Xinliang ^a   Tan, Yonghong ^b  

^a HENAN POLYTECHNIC UNIVERSITY   (China)

^b SHANGHAI NORMAL UNIVERSITY   (China)

Author keywords

First order differential operator; Hysteresis; Hysteretic operator; Neural networks; Piezoelectric actuator; Rate dependent

Indexed keywords

DIFFERENTIAL OPERATORS; DYNAMIC BEHAVIORS; DYNAMIC CHARACTERISTICS; FIRST-ORDER; HYBRID MODEL; HYSTERESIS PHENOMENON; HYSTERETIC OPERATOR; INPUT SPACE; NONLINEAR CHARACTERISTICS; SUBMODELS;

CANTILEVER BEAMS; DAMPING; DIFFERENTIAL EQUATIONS; FEATURE EXTRACTION; HYBRID SENSORS; HYSTERESIS; MATHEMATICAL OPERATORS; NEURAL NETWORKS; PIEZOELECTRICITY; STIFFNESS;

PIEZOELECTRIC ACTUATORS;

EID: 76449090315     PISSN: 09244247     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sna.2009.10.009     Document Type: Article

References (21)

1. Ge P., and Jouaneh M. Tracking control of a piezoceramic actuator. IEEE Trans. Control Syst. Tech. 4 3 (1996) 206-219
2. Tzen J.J., Jeng S.L., and Chieng W.H. Modeling of piezoelectric actuator for compensation and control design. Prec. Eng. 27 1 (2003) 70-86
3. Hu H., and Mrad R.B. On the classical Preisach model for hysteresis in piezoceramic actuators. Mechatronics 13 85 (2003) 94
4. Ge P., and Jouaneh M. Generalized Preisach model for hysteresis nonlinearity of piezoceramic actuators. Prec. Eng. 20 99 (1997) 111
5. Hu H., Georgiou H.M.S., and Mrad R.B. Enhancement of tracking ability in piezoelectric actuators subject to dynamic excitation conditions. IEEE/ASME Trans. Mechatron. 10 2 (2005) 230-239
6. Song D., and Li C.J. Modeling of piezo actuator's nonlinear and frequency dependent dynamics. Mechatronics 9 391 (1999) 410
7. Mrad R.B., and Hu H. A model for voltage-to-displacement dynamics in piezoceramic actuator subject to dynamic-voltage excitations. IEEE/ASME Trans. Mechatron. 7 4 (2002) 479-489
8. Yu Y., Xiao Z., Naganathan N.G., and Dukkipati R.V. Dynamic Preisach modeling of hysteresis for the piezoceramic actuator system. Mech. Machine Theory 37 75 (2002) 89
9. Ang W.T., Khosla P.K., and Riviere C.N. Feedforward controller with inverse rate-dependent model for piezoelectric actuators in trajectory-tracking applications. IEEE/ASME Trans. Mechatron. 12 2 (2007) 134-142
10. Krejci P., and Kuhnen K. Inverse control of systems with hysteresis and creep. IEE Proc. Control Theory Appl. 148 3 (2001) 185-192
11. Ru C., and Sun L. Hysteresis and creep compensation for piezoelectric actuator in open-loop operatoion. Sensors Actuat. A: Phys. 122 1 (2005) 123-130
12. Cincotti S., and Daneri I. Neural network identification of a nonlinear circuit model of hysteresis. Electron. Lett. 33 13 (1997) 1154-1156
13. Hwang C.L., Jan C., and Chen Y.H. Piezomechanics using intelligent variable-structure control. IEEE Trans. Indus. Electron. 48 1 (2001) 47-59
14. Li C., and Tan Y. A neural networks model for hysteresis nonlinearity. Sensors Actuat. Phys. A 112 1 (2004) 49-54
15. Zhao X., and Tan Y. Modeling hysteresis and its inverse model using neural networks based on expanded input space method. IEEE Trans. Contr. Syst. Tech. 16 3 (2008) 484-490
16. Dong R., Tan Y., Chen H., and Xie Y. A neural networks based model for rate-dependent hysteresis for piezoceramic actuators. Sensors Actuat. A: Phys. 143 2 (2008) 370-376
17. Deng L., and Tan Y. Diagonal recurrent neural network with modified backlash operators for modeling of rate-dependent hysteresis in piezoelectric actuators. Sensors Actuat. A: Phys. 148 1 (2008) 259-270
18. Armstrong-Helouvry B. Control of Machines with Friction vol. 2 (1991), Kluwer, New York
19. R.B. Gorbert, Control of hysteretic systems with Preisach representation, Ph.D Thesis of University of Waterloo, Ontario, Canada, 1997.
20. Funahashi K.L. On the approximate realization of continuous mappings by neural networks. Neural Networks 2 (1989) 183-192
21. Hagan M.T., and Menhaj M.B. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks 5 6 (1994) 889-993