Neural Network Based Modeling of Hysteresis in Smart Material Based Sensors
Hysteresis is a nonlinear phenomenon which is involved with dynamics, non-smoothness and multi-valued mapping. It usually exists in elastic materials, smart materials, and energy-storage materials. For describing the characteristic of hysteresis, a basis function based neural network model is proposed in this paper. In this method, the multi-valued mapping of hysteresis is transferred into a one-to-one mapping with an expanded input space involving the input variable and a constructed hysteretic auxiliary function. Thus, the neural network can be employed to approximate the characteristic of hysteresis. Finally, the method is used to the modeling of hysteresis in a smart material based sensor.
KeywordsHysteresis Expanded input space Neural network Modeling
The work presented in this paper has been funded by the National Science Foundation of China under Grants 61671303 and 61571302, the Open Fund of the Key Laboratory of Nano-Devices and Applications, Chinese Academy of Sciences under Grant 18ZS06, the Shanghai Pujiang Program under Grant 18PJ1400100, the Natural Science Foundation of Shanghai under Grant 16ZR1446700, and the project of the Science and Technology Commission of Shanghai under Grant 18070503000.
- 1.Hu, H.: Compensation of hysteresis in piezoceramic actuators and control of nanopositioning system. Ph.D. thesis of University of Toronto, Canada (2003)Google Scholar
- 2.Shahinpoor, M., Kim, K.: Ionic polymer-metal composites. Part I. Fundamentals. Smart Mater. Struct. 10, 819–833 (2001)Google Scholar
- 3.Mayergoyz, D.: Dynamic Preisach models of hysteresis. IEEE Trans. Magnetics 24(6), 2925–2927 (1988)Google Scholar
- 4.Awrejcewicz, J., Dzyubak, L., Lamarque, C.: Modelling of hysteresis using Masing–Bouc-Wen’s framework and search of conditions for the chaotic responses. Commun. Nonlinear Sci. Numer. Simul. 13, 939–958 (2008)Google Scholar
- 5.Yu, Y., Naganathan, N., Dukkipati, R.: Preisach modeling of hysteresis for piezoceramic actuator system. Mech. Mach. Theory 37(1), 49–59 (2002)Google Scholar
- 6.Su, C., Wang, Q., Chen, X.: Adaptive variable structure control of a class of nonlinear systems with unknown Prandtl-Ishlinskii hysteresis. IEEE Trans. Autom. Control 50(12), 2069–2074 (2005)Google Scholar
- 7.Dong, R., Tan, Y., Chen, H., Xie, Y.: A neural networks based model for rate-dependent hysteresis for piezoceramic actuators. Sens. Actuators A 143(2), 370–376 (2008)Google Scholar
- 8.Deng, L., Tan, Y.: Diagonal recurrent neural network with modified backlash operators for modeling of rate-dependent hysteresis in piezoelectric actuators. Sens. Actuators A 148(1), 259–270 (2008)Google Scholar
- 9.Chen, X., Zhu, G., Yang, X., Hung, D., Tan, X.: Model-based estimation of flow characteristics using an ionic polymer-metal composite beam. IEEE/ASME Trans. Mechatron. 18(3), 932–943 (2013)Google Scholar
- 10.Zhao, X., Tan, Y.: Neural network based identification of Preisach-type hysteresis in piezoelectric actuator using hysteretic operator. Sens. Actuators A 126, 306–311 (2006)Google Scholar
- 11.Nam, D., Ahn, K.: Identification of an ionic polymer metal composite actuator employing Preisach type fuzzy NARX model and Particle Swarm Optimization. Sens. Actuators A 183, 105–114 (2012)Google Scholar
- 12.Li, Z., Hao, L.: The identification of discrete Preisach model based on IPMC. In: Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, vol. 21–26, pp. 751–755 (2009)Google Scholar