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Volume 06 Issue 11 November 2023

Optimization of Field-Oriented Control for PMSM Motor
1Ng Soon King, 2Chua Hong Siang, 3Ting Sie King
1,2,3Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia
DOI : https://doi.org/10.47191/ijmra/v6-i11-33

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ABSTRACT:

This research focuses on designing and simulating a sensorless control technique for optimising field-oriented control to achieve an optimization control applicable to water pressure regulation management. In the irrigation system, water pressure changes are determined by the number of activated emitters operating at different times. Unstable water pressure may reduce irrigation efficiency, leaving brown areas of the landscape not receiving adequate watering. Thus, the Field-oriented control technique is introduced in the paper to regulate the water pressure in the water pump controller using a permanent magnet synchronous motor (PMSM). The PSO-SFC field-oriented control technique for the PMSM was studied to improve motor efficiency and precision motor control over the torque and speed. The cascade-free state feedback controller (SFC) can achieve better dynamics and disturbance compensation in the controller. The SFC's tuning process has been achieved by using a linear-quadratic optimization method and PSO (particle swarm optimization) in the cascaded process. The process improves the coefficient of gain K produced by linear-quadratic optimization and PSO to obtain promising Q and R matrices in the adaptive system with improved output response in the simulation.

KEYWORDS:

Field-oriented control; water pressure; permanent magnet synchronous motor; state feedback controller; particle swarm optimization

REFERENCES
1) Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agriculture, 12(10), 1745. https://www.mdpi.com/2077-0472/12/10/1745

2) Ghoreishi, A., & Nekoui, M. (2012). Optimal Weighting Matrices Design for LQR Controller Based on Genetic Algorithm and PSO. Advanced Materials Research, 433-440, 7546-7553. https://doi.org/10.4028/www.scientific.net/AMR.433-440.7546

3) Ghoreishi, A., Nekoui, M., & Basiri, S. (2011). Optimal Design of LQR Weighting Matrices based on Intelligent Optimization Methods. International Journal of Intelligent Information Processing, 2. https://doi.org/10.4156/ijiip.vol2.issue1.7

4) Gopal B T, V. (2017). Comparison Between Direct and Indirect Field Oriented Control of Induction Motor. International Journal of Engineering Trends and Technology, 43, 364-369. https://doi.org/10.14445/22315381/IJETT-V43P260

5) Grofu, F. (2021). FIELD ORIENTED CONTROL (FOC) FOR BLDC MOTOR. Fiability & Durability / Fiabilitate si Durabilitate, 27(1), 124-129.

6) Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95-international conference on neural networks,

7) Neto, J. V. d. F., & Bottura, C. P. (1999, 6-9 July 1999). Parallel genetic algorithm fitness function team for eigenstructure assignment via LQR designs. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406),

8) Paponpen, K., & Konghirun, M. (2015, 15-17 June 2015). LQR state feedback controller based on particle swarm optimization for IPMSM drive system. 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA),

9) Song, Z., Xiao, D., & Rahman, F. (2014). Online particle swarm optimization for sensorless IPMSM drives considering parameter variation. https://doi.org/10.1109/IPEC.2014.6869970

10) Szczepański, R., Tarczewski, T., & Grzesiak, L. M. (2020). PMSM drive with adaptive state feedback speed controller. Bulletin of The Polish Academy of Sciences-technical Sciences.

11) Zhao, Y. (2014). Position/Speed Sensorless Control for Permanent-Magnet Synchronous Machines University of Nebraska, Lincoln, NE]. DigitalCommons.
Volume 06 Issue 11 November 2023

There is an Open Access article, distributed under the term of the Creative Commons Attribution – Non Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting and building upon the work for non-commercial use, provided the original work is properly cited.


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