Optimization of the sensitivity of the magnetoimpedance sensor of small magnetic fields by methods of sequential approximation and swarm of particles

The use of multiparametric optimization of an unknown discrete function in the development of applied solutions for physical systems is considered. Such optimization is practically implemented in real time using modern data transfer protocols at high speed and continuously increasing computing power. To optimize the sensitivity of a modern magnetic sensor based on high-frequency magnetoimpedance in ferromagnetic microconducts, an iterative method of global maximum search, the particle swarm algorithm, has been applied. The output signal of the sensor depends non-linearly on both the internal magnetic properties of the microcircuit and the excitation mode, which requires a certain calibration to establish optimal excitation parameters. The sensor output signals for various excitation parameters and external magnetic fields were measured using an automated installation. The results of the search for the global maximum by the sequential approximation method and the particle swarm method presented in the paper demonstrate the effectiveness of the search algorithm used, the particle swarm algorithm turned out to be the most effective, since it found the global maximum more accurately. With different excitation parameters, the algorithm has always determined the maximum sensitivity when varying the three main parameters of the excitation signal: frequency, amplitude and constant component. The results obtained can be applied in the development of highly sensitive intelligent magnetic sensors and systems based on them.

1. Xie G., Sunden B., Wang Q., Applied Thermal Engineering, 2008, vol. 28, no. 8-9, pp. 895–906. https://doi.org/10.1016/j.applthermaleng.2007.07.008

2. Abdulwhab A. et al., Electric Power Components and Systems, 2004, vol. 32, no. 12, pp. 1239–1254. https://doi.org/10.1080/15325000490446601

3. Jahangirian A., Shahrokhi A., Computers & Fluids, 2011, vol. 46, no. 1, pp. 270–276. https://doi.org/10.1016/j.compfluid.2011.02.010

4. Diab A. A. Z. et al., Energy Reports, 2022, vol. 8, no. 10, pp. 384–393. https://doi.org/10.1016/j.egyr.2022.05.168

5. Kennedy J., Eberhart R. C., Shi Y., Swarm Intelligence, Morgan Kaufmann Publishers, San Francisco, Calif, USA, 2001, pp. 287–368.

6. Poli R., Journal of Artifi cial Evolution and Applications, 2008, vol. 10, pp. 1–10. https://doi.org/10.1155/2008/685175

7. Gerginov V., Pomponio M., Knappe S., IEEE Sensors Journal, 2020, vol. 20, no. 21, pp. 12684–12690. https://doi.org/10.1109/jsen.2020.300219

8. Zhang R., Mhaskar R., Smith K., Prouty M., Applied Physics Letters, 2020, vol. 116, no. 14, pp. 1–5. https://doi.org/10.1063/5.0004746

9. Han C. et al., Virtual Reality & Intelligent Hardware, 2022, vol. 4, no. 1, pp. 38–54. https://doi.org/10.1016/j.vrih.2022.01.003

10. Tehranchi M. M., Ranjbaran M., Eftekhari H., Sensors and Actuators A: Physical, 2011, vol. 170, no. 1–2, pp. 55–61. https://doi.org/10.1016/j.sna.2011.05.031

11. Zhang D. et al., Sensors and Actuators A: Physical, 2016, vol. 249, no. 1, pp. 225–230. https://doi.org/10.1016/j.sna.2016.09.005

12. Tsuyoshi U., Jiaju M., Journal of Magnetism and Magnetic Materials, 2020, vol. 514, no. 15, pp. 1–7. https://doi.org/10.1016/j.jmmm.2020.167148

13. Makhnovskiy D., Panina L., Mapps D. J., Physical Review B, 2002, vol. 63, pp. 1–17. https://doi.org/10.1103/PhysRevB.63.144424

14. Ipatov M., Zhukova V., Zhukov A., et al., Applied Physics Letters. AIP Publishing, 2010, vol. 97, no. 25, pp. 1–4. https://doi.org/10.1063/1.3529946

15. Zhukov A. et al., Journal of Alloys and Compounds, 2019, vol. 814, pp. 1–17. https://doi.org/10.1016/j.jallcom.2019.152225

16. Gudoshnikov S. et al., Physica Status Solidi A, 2014, vol. 211, no. 5, pp. 980–985. https://doi.org/10.1002/pssa.201300717

17. Panina L. V., et al., Physica Status Solidi A, 2015, vol. 213, no. 2, pp. 341-349. https://doi.org/10.1002/pssa.201532578