Abstract
Piezoelectric ceramics (PZT) actuator has been widely used in flexure-guided micro/nanopositioning stage because of their high resolution. However, it is quite hard to achieve high-rate precision positioning control because of the complex hysteresis nonlinearity effect of PZT actuator. Thus, an online RELM algorithm with forgetting property (FReOS-ELM) is proposed to handle this issue. Firstly, we adopt regularized extreme learning machine (RELM) to build an intelligent hysteresis model. The training of the algorithm is completed only in one step, which avoids the shortcomings of the traditional hysteresis model based on artificial neural network (ANN) that slow training speed and easy to fall into the local minimum. Then, based on the regularized online sequential extreme learning machine (ReOS-ELM), an online RELM algorithm with forgetting property (FReOS-ELM) is designed, which can avoid the computational load of ReOS-ELM in the process of adding new data for learning online. In the experiment, a real-time voltage signal with varying frequencies and amplitudes is adopted, and the output displacement data of the micro/nanopositioning stage is also acquired and analyzed. The experimental results show that the RELM-based hysteresis modeling algorithm has higher efficiency and more stable learning ability and generalization ability than the traditional neural network. In the aspect of online modeling, FReOS-ELM hysteresis modeling can achieve a better result than ReOS-ELM.
Similar content being viewed by others
References
Tang H, Li Y (March 2014) Development and active disturbance rejection control of a compliant micro-/nanopositioning piezostage with dual mode. IEEE Trans Ind Electron 61(3):1475–1492
Yang W, Lee S-Y, You B-J (2010) A piezoelectric actuator with a motion-decoupling amplifier for optical disk drives. Smart Mater. Struct, May
Tao F, Zuo Y, Xu L, Zhang L (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Transactions on Industrial Informatics 10(2):1547–1557
H. Song, G. Vdovin, R. Fraanje, G. Schitter, and M. Verhaegen, Extracting hysteresis from nonlinear measurement of wavefront-sensorless adaptive optics system, May 2009: 61–63.
Tao F, Bi L, Zuo Y, Nee A (2016) A hybrid group leader algorithm for green material selection with energy consideration in product design. CIRP Annals-Manufacturing Technology 65(1):9–12
H. Tang and Y. Li, A new flexure-based Y nanomanipulator with nanometer scale positioning resolution and millimeter range workspace, IEEE-ASME Transactions on Mechatronics, vol. 20, no. 3, June .2015:1320–1330.
Tao F, Zhao D, Hu Y, Zhou Z (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Transactions on Industrial Informatics 4(4):315–327
Guo-Ying G, Zhu L-M, Chun-Yi S (2014) Modeling and compensation of asymmetric hysteresis nonlinearity for piezoceramic actuators with a modified Prandtl-Ishlinskii model. IEEE Trans Ind Electron 61(3):1583–1595
Yan LZ, Ming LH, Meng YY, Cheng YS (2012) Automatic hysteresis modeling of piezoelectric micromanipulator in vision-guided micromanipulation systems. IEEE/ASME Transactions on Mechatronics 17(3):547–553
Wu Y, Fang Y, Ren X et al (2016) Back propagation neural networks based hysteresis modeling and compensation for a piezoelectric scanner. IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale, July:119–124
Othman YS, Mahmood IA, Aibinu AM et al (2012) Frequency based hysteresis compensation for piezoelectric tube scanner using artificial neural networks. Procedia Engineering 41:757–763
Ma Y, Zhang X, Xu M et al (2013) Hybrid model based on Preisach and support vector machine for novel dual-stack piezoelectric actuator. Mechanical Systems & Signal Processing 34(1–2):156–172
Tao F, Guo H, Zhang L, Cheng Y (2012) Modelling of combinable relationship-based composition service network and theoretical proof of its scale-free characteristics. Enterprise Information Systems 6(4):373–404
Rebai A, Guesmi K, Hemici B (2016) Adaptive fuzzy synergetic control for nonlinear hysteretic systems. Nonlinear Dynamics:1–10
Liaw HC, Shirinzadeh B (2009) Neural network motion tracking control of piezo-actuated flexure-based mechanisms for micro-/nanomanipulation. IEEE/ASME Transactions on Mechatronics 14(5):517–527
Liu W, Cheng L, Hou Z-G et al (2016) An inversion-free predictive controller for piezoelectric actuators based on a dynamic linearized neural network model. IEEE/ASME Transactions on Mechatronics 21(1):214–226
Zhang X, Li Z, Su CY, Lin Y (2016) Implementable adaptive inverse control of hysteretic systems via output feedback, with application to piezoelectric positioning stages. IEEE Trans Ind Electron 63(9):1–1
Huang G, Huang GB, Song SJ, You KY (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48
Li J, Tao F, Cheng Y, Zhao L (2015) Big data in product lifecycle management. International Journal of Advanced Manufacturing Technology 81(1):667–684
Zhang Y, Tan N (2010) Weights direct determination of feedforward neural networks without iterative BP-training. International Conference on Communications:59–63
Tao F, Laili Y, Xu L, Zhang L (2013) FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Transactions on Industrial Informatics 9(4):2023–2033
Tao F, Cheng J, Cheng Y, Gu S, Zheng TY, Yang H (2017) SDMSim: a manufacturing service supply–demand matching simulator under cloud environment. Robotics and Computer Integrated Manufacturing 45(6):34–46
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
Huynh HT, Wona Y (2011) Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks. Pattern Recogn Lett 32(14):1930–1935
Edardar M, Tan X, Khalil HK (2015) Design and analysis of sliding mode controller under approximate hysteresis compensation. IEEE Trans Control Syst Technol 23(2):598–608
Li W, Chen X, Li Z (2013) Inverse compensation for hysteresis in piezoelectric actuator using an asymmetric rate-dependent model. Rev Sci Instrum 84(11):115003
Peng JY, Chen XB (2012) Novel models for one-sided hysteresis of piezoelectric actuators. Mechatronics 22(6):757–765
Wang X, Han M (2014) Online sequential extreme learning machine with kernels for nonstationary time series prediction. Neurocomputing 145(145):90–99
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wu, Z., Tang, H., He, S. et al. Fast dynamic hysteresis modeling using a regularized online sequential extreme learning machine with forgetting property. Int J Adv Manuf Technol 94, 3473–3484 (2018). https://doi.org/10.1007/s00170-017-0549-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-017-0549-x