Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator
Source abstract
Abstract
There is a serious nonlinear relationship between input and output in the giant magnetostrictive actuator (GMA) and how to establish mathematical model and identify its parameters is very important to study characteristics and improve control accuracy. The current-displacement model is firstly built based on Jiles-Atherton (J-A) model theory, Ampere loop theorem and stress-magnetism coupling model. And then laws between unknown parameters and hysteresis loops are studied to determine the data-taking scope. The modified simulated annealing differential evolution algorithm (MSADEA) is proposed by taking full advantage of differential evolution algorithm’s fast convergence and simulated annealing algorithm’s jumping property to enhance the convergence speed and performance. Simulation and experiment results shows that this algorithm is not only simple and efficient, but also has fast convergence speed and high identification accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Magnetic Properties and Applications · Electric Motor Design and Analysis · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automation Science and Electrical Engineering, Beihang University , No. 37 Xueyuan road, Haidian district, Beijing 100191, P. R. China
Funding
National Natural Science Foundation of China
References (24)
Show source-provided references
1. A low frequency magnetostrictive inertial actuator for vibration control10.1016/j.sna.2012.03.015 ↗
2. An approach to design and fabrication of resonant giant magnetostrictive transducer
3. Magnetostrictive stress induced frequency shift in resonator for magnetic field sensor10.1016/j.sna.2016.06.041 ↗
4. Theory of magnetization process in ferromagnets and its application to magnetic mechanical effect10.1088/0022-3727/17/6/023 ↗
5. Theory of magnetomechanical effect10.1088/0022-3727/28/8/001 ↗
6. Dynamics of domain magnetization and the Barkhausen effect10.1023/a:1022846128461 ↗
7. Giant magnetostrictive actuator nonlinear dynamic Jiles–Atherton model10.1016/j.sna.2016.09.009 ↗
8. Energy-based hysteresis model for magnetostrictive transducers10.1109/20.825804 ↗
9. Research of Jiles-Atherton dynamic model in giant magnetostrictive actuator
10. A self-adaptive genetic algorithm to estimate J-A model parameters considering minor loops10.1016/j.jmmm.2014.08.084 ↗
11. Parameter identification of hysteresis model for GMA based on improved PSO algorithm
12. Identification of parameters of the Jiles-Atherton model by neural networks
13. Optimization of Jiles-Atherton hysteresis model parameters using Taguchi’s method10.1109/tmag.2014.2357394 ↗
14. Theory of ferromagnetic hysteresis10.1063/1.333582 ↗
15. Frequency dependence of hysteresis curves in conducting magnetic materials10.1063/1.358399 ↗
16. Queries on the J-A modeling theory of the magnetization process in ferromagnets and proposed correction method
17. Energy based hysteresis model for magnetostrictive transducers10.1109/20.825804 ↗
18. A nonlinear constitutive model for Terfenol-D rods10.1063/1.1850618 ↗
19. Equation of state calculations by fast computing machines10.1063/1.1699114 ↗
20. Optimization by simulated annealing
21. Parameter identification based on modified annealing algorithm for articulated arm CMMS
22. Differential evolution-a simple and efficient heuristic for global optimization over continuous space10.1023/a:1008202821328 ↗
23. Differential evolution design of an II R-filter
24. Adaptive coupling of differential evolution and multiquadrics approximation for the tuning of the optimization process10.1109/20.877621 ↗