Abstract
In this paper, a constraint factor (CF) is presented. The CF and an odd m-order polynomial form a new hysteretic operator (HO) together. And then, an expanded input space is constructed based on the proposed HO. In the expanded input and output spaces, the one-to-multiple mapping of hysteresis is transformed into a one-to-one mapping so that a neural network can be used to develop a neural hysteresis model. The model parameters are computed by using the least square method. Finally, the neural hysteresis model is employed to approximate a real data from a magnetostrictive actuator in an experiment. The experimental results demonstrate the proposed approach is effective.
This work is supported in part by National Natural Science Foundation of China (Grant nos. 11304282, 61540034, 61304015, and 61273184); Zhejiang Provincial Natural Science Foundation (Grant nos. LQ14F050002, LQ16F030002, and LY15F030022); Science Technology Department of Zhejiang Province (Grant no. 2014C31020); Pre-research Special Foundation for Interdisciplinary Subject at Zhejiang University of Science and Technology (Grant no. 2014JC03Y).
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Shen, Y., Ma, L., Li, J., Zhang, X., Zhao, X., Zheng, H. (2016). A Neural Hysteresis Model for Smart-Materials-Based Actuators. In: Kubota, N., Kiguchi, K., Liu, H., Obo, T. (eds) Intelligent Robotics and Applications. ICIRA 2016. Lecture Notes in Computer Science(), vol 9834. Springer, Cham. https://doi.org/10.1007/978-3-319-43506-0_57
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