Hysteresis compensation for giant magnetostrictive actuators using dynamic recurrent neural network
According to the hysteresis characteristics of the giant magnetostrictive actuator (MA), a dynamic recurrent neural network (DRNN) is constructed as the inverse hysteresis model of the MA, and an on-line hysteresis compensation control strategy combining the DRNN inverse compensator and a proportion...
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| Veröffentlicht in: | IEEE transactions on magnetics 2006-04, Vol.42 (4), p.1143-1146 |
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| container_end_page | 1146 |
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| container_issue | 4 |
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| container_title | IEEE transactions on magnetics |
| container_volume | 42 |
| creator | Shuying Cao, Shuying Cao Boweng Wang, Boweng Wang Jiaju Zheng, Jiaju Zheng Wenmei Huang, Wenmei Huang Ling Weng, Ling Weng Weili Yan, Weili Yan |
| description | According to the hysteresis characteristics of the giant magnetostrictive actuator (MA), a dynamic recurrent neural network (DRNN) is constructed as the inverse hysteresis model of the MA, and an on-line hysteresis compensation control strategy combining the DRNN inverse compensator and a proportional derivative (PD) controller is used for precision position tracking of the MA. Simulation results validate the excellent performances of the proposed strategy |
| doi_str_mv | 10.1109/TMAG.2006.871464 |
| format | Article |
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| ispartof | IEEE transactions on magnetics, 2006-04, Vol.42 (4), p.1143-1146 |
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| subjects | Actuators Compensation Cross-disciplinary physics: materials science rheology Dynamic recurrent neural network (DRNN) Dynamics Exact sciences and technology Frequency Fuzzy control Hysteresis Inverse inverse compensator Inverse problems Magnetic hysteresis Magnetism Magnetostriction magnetostrictive actuator (MA) Materials science Other topics in materials science PD control Physics Proportional control Recurrent neural networks Saturation magnetization Strategy |
| title | Hysteresis compensation for giant magnetostrictive actuators using dynamic recurrent neural network |
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