Hysteretic neural network modeling of spring-coupled piezoelectric actuators

作者：Lien J P^*; Fang Tiegang; Buckner Gregory D

来源：Smart Materials and Structures, 2011, 20(6): 065007.

DOI：10.1088/0964-1726/20/6/065007

This paper discusses the development of a high-fidelity, computationally efficient model for spring-coupled piezoelectric stack actuators. The model is based on a hysteretic recurrent neural network (HRNN), and aims to balance computational tractability with physical intuition. Previous work has detailed the development and experimental validation of an HRNN model for unloaded piezoelectric actuators. This paper extends the modeling approach to incorporate coupling with linear springs, and discusses training techniques based on genetic algorithms, which provide advantages over the previously employed Levenberg-Marquardt methods in terms of accuracy and model complexity. The resulting models are computable in real time. Model validity is established by comparison with a rate-dependent threshold-discrete Prandtl-Ishlinskii model.

• 出版日期2011-6