English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Hysteresis exists in magnetic shape memory alloy (MSMA) actuators, which restricts MSMA actuators’ application. To describe hysteresis of the MSMA actuators, a hysteresis model based on the radial basis function neural network (RBFNN) is put forward. Then, an inverse RBFNN model is set up, and it is compared with the inverse model based on the traditional cut-and-try method. Finally, to solve hysteresis of the actuators, an inverse model for MSMA actuators is used to build feed-forward controller. Simulation results show the maximum modeling error for inverse hysteresis model designed by neural network is 0.79% and compared with traditional cut-and-try method, the maximum modeling error decreases by 1.85%. The maximum tracking error rate of feed-forward control is 0.38%. The hysteresis of MSMA actuators is reduced. By using the feed-forward controller, high precision control is achieved.

Feed-Forward Control for Magnetic Shape Memory Alloy Actuators Based on the Radial Basis Function Neural Network Model