A hopfield neural network-based Bouc-Wen model for magnetic shape memory alloy actuator
Source abstract
Abstract
Magnetic shape memory alloy (MSMA) actuator has potential application value in the aerospace, robotics and precision positioning due to the advantages such as small size, high precision, long stroke length and large energy density. However, the asymmetrical rate-dependent hysteresis between input and output of the MSMA actuator makes it difficult to build precise model of the MSMA actuator-based micropositioning system, so that the application of the MSMA actuator is seriously hindered. In this paper, a Bouc-Wen (BW) model is adopted to describe the hysteresis of the MSMA actuator. The parameters of BW model are identified online by Hopfield neural network (HNN). Then, the effectiveness of HNN-based BW model is fully certified using the experiments. The experimental results show that the BW model identified in this paper can accurately describe the hysteresis of the MSMA actuator at different input excitation.
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Research context
OpenAlex topics: Piezoelectric Actuators and Control · Shape Memory Alloy Transformations · Iterative Learning Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Control Science and Engineering, Jilin University , Changchun 130022, China
Funding
Innovative Research Group Project of the National Natural Science Foundation of China · Department of Science and Technology of Jilin Province
References (25)
Show source-provided references
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