Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform
Source abstract
Abstract
In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Magnetic Bearings and Levitation Dynamics · Engineering and Technology Innovations · Inertial Sensor and Navigation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Instituto Superior de Engenharia do Porto (ISEP), Rue Dr. António Bernardino de Almeida 431, 4200-072 Porto, Portugal
References (35)
Show source-provided references
1. Åström, K.J., and Hägglund, T. (1995). PID Controllers: Theory, Design, and Tuning, Instrument Society of America. [2nd ed.].
2. Tuning of PID controllers based on Bode’s ideal transfer function10.1007/s11071-004-3763-7 ↗
3. Passino, K.M., and Yurkovich, S. (1998). Fuzzy Control, Addison-Wesley.
4. Passino, K.M. (2005). Biomimicry for Optimization, Control, and Automation, Springer.
5. Haykin, S. (2009). Neural Networks and Learning Machines, Pearson. [3rd ed.].
6. Genetic Optimization of Fuzzy Fractional PD+I Controllers10.1016/j.isatra.2015.01.006 ↗
7. Systematic evaluation of convolution neural network advances on the Imagenet10.1016/j.cviu.2017.05.007 ↗
8. Analysis of the proficiency of fully connected neural networks in the process of classifying digital images. Benchmark of different classification algorithms on high-level image features from convolutional layers10.1016/j.eswa.2019.05.058 ↗
9. Chen, Y., Shi, Y., and Zhang, B. (2019, January 6–9). Optimal Control Via Neural Networks: A Convex Approach. Proceedings of the ICLR 2019—International Conference on Learning Representations, New Orleans, LA, USA.
10. Observer-based adaptive fuzzy-neural-network control for hybrid maglev transportation system10.1016/j.neucom.2015.10.006 ↗
11. Neural networks for control
12. An introduction to the use of neural networks in control systems10.1002/rnc.727 ↗
13. Zhao, S.T., and Gao, X.W. (June, January 31). Neural network adaptive state feedback control of a magnetic levitation system. Proceedings of the 26th Chinese Control and Decision Conference, CCDC 2014, Changsha, China.10.1109/CCDC.2014.6852423 ↗
14. Modeling and control with neural networks for a magnetic levitation system10.1016/j.neucom.2016.09.101 ↗
15. Electromagnetic suspension and levitation10.1088/0034-4885/44/4/002 ↗
16. Design and implementation of a 2-DOF PID compensation for magnetic levitation systems10.1016/j.isatra.2014.05.015 ↗
17. Hyung-Suk, H., and Dong-Sung, K. (2016). Magnetic Levitation—Maglev Technology and Applications, Springer.
18. Nonlinear model predictive control of a magnetic levitation system10.1016/j.conengprac.2013.04.009 ↗
19. Real time implementation of fractional order PID controllers for a magnetic levitation plant10.1016/j.aeue.2017.05.029 ↗
20. Application of new training methods for neural model reference control10.1016/j.engappai.2018.07.005 ↗
21. A modeling and control approach to magnetic levitation system based on state-dependent ARX model10.1016/j.jprocont.2013.10.016 ↗
22. Favela-Contreras, A. Output feedback control of a mechanical system using magnetic levitation10.1016/j.isatra.2015.01.012 ↗
23. Robust switched control of an air levitation system with minimum sensing10.1016/j.isatra.2019.06.020 ↗
24. Chacon, J., Saenz, J., de la Torre, L., Diaz, J.M., and Esquembre, F. (2017). Design of a Low-Cost Air Levitation System for Teaching Control Engineering. Sensors, 17.10.3390/s17102321 ↗
25. Microsystems, A. (2020, December 09). A1324-A1325-A1326: Low Noise, Linear Hall Effect Sensor ICs. Available online: https://www.allegromicro.com/en/Products/Sense/Linear-and-Angular-Position/Linear-Position-Sensor-ICs/A1324-5-6.
26. Microsystems, A. (2020, May 22). Datasheet ACS711. Available online: https://static6.arrow.com/aropdfconversion/31faf7d62603aaa2b659e4e2e96e413e49963d95/acs711-datasheet.pdf.
27. (2020, December 31). Simulink Support Package for Arduino Hardware. Available online: https://www.mathworks.com/matlabcentral/fileexchange/40312-simulink-support-package-for-arduino-hardware.
28. Zeltom LLC (2009). Electromagnetic Levitation System, Zeltom LLC.. Technical report.
29. Microchip (2020, May 22). ATmega640/1280/1281/2560/2561 Datasheet. Available online: https://ww1.microchip.com/downloads/en/DeviceDoc/ATmega640-1280-1281-2560-2561-Datasheet-DS40002211A.pdf.
30. A Simple Analog Controller for a Magnetic Levitation Kit
31. Ogata, K. (2004). System Dynamics, Prentice Hall. [4th ed.].
32. Ogata, K. (2010). Modern Control Engineering, Prentice Hall. [5th ed.].
33. William, J.P. (2010). System Dynamics, The McGraw-Hill. [3th ed.].
34. Beale, G. (2020, May 25). Classical Systems and Control Theory-Compensator Design to Improve Steady-State Error Using Root Locus. Available online: https://people-ece.vse.gmu.edu/~gbeale/ece_421/comp_root_ess.pdf.
35. Kajan, S. (2008, January 10–13). Neural controllers for nonlinear systems in Matlab. Proceedings of the 16th Annual Conference on Technical Computing, Prague, Czech.