Artificial intelligence for field-oriented control on light rail transit Jabodebek

Wahyu Pribadi; Ricto Yudi Wicaksono; Rakhmad Gusta Putra; Darma Arif Wicaksono; Moh. Lutfi Yazid; Wahyu Pribadi; Ricto Yudi Wicaksono; Rakhmad Gusta Putra; Darma Arif Wicaksono; Moh. Lutfi Yazid

doi:10.55981/j.mev.2025.1061

Artificial intelligence for field-oriented control on light rail transit Jabodebek

Wahyu Pribadi, Ricto Yudi Wicaksono, Rakhmad Gusta Putra, Darma Arif Wicaksono, Moh. Lutfi Yazid

Abstract

The increasing number of vehicles in Jakarta, Indonesia, has had a negative impact on the environment. If this trend continues, it may significantly harm public health. In response to this issue, the government has introduced mass transportation solutions, such as the Jabodebek light rail transit (LRT) system. One of the key technical challenges in operating the LRT is ensuring smooth and reliable traction motor control. This study presents a simulation of the Jabodebek LRT’s traction motor performance when traversing a hilly route with a 29 ‰ gradient. A field-oriented control (FOC) method is implemented to regulate motor speed. The train operates under a constant load, with its weight gradually increasing from the lowest to the highest point of the slope. Two tuning methods are applied to optimize the controller parameters: manual (hand-tuning) and artificial intelligence-based optimization using the Firefly algorithm and the Grey Wolf optimizer (GWO). The integral of time multiplied by absolute error (ITAE) is used as the objective function to evaluate the speed control performance. The simulation results show that the Grey Wolf optimizer delivers the best performance, achieving stable speed control despite load disturbances. The optimal proportional and integral gains obtained are Kp = 16.233861 and Ki = 0.526774, respectively.

10.55981/j.mev.2025.1061

Keywords

field-oriented control; firefly algorithm; Grey Wolf algorithm; integral of time multiplied by absolute error (ITAE); LRT Jabodebek

Full Text:

PDF

References

M. Thoriq Maulana, M. Hilmi Habibullah, Sunandar, N. Sholihah, M. Ainul Rifqi L. P., and F. Fahrudin, “Kegiatan pemantauan kualitas udara provinsi DKI Jakarta tahun 2022,” Lap. Akhir, vol. 1, no. 201310200311137, pp. 78–79, 2015, [Online].

B. P. Statistik, “Berita resmi statistik No.11/03/31/Th.XXVI, 1 Maret 2024,” vol. 2023, no. 11, pp. 1–8, 2024, [Online].

D. Pribula, A. Dolinayova, and M. Kendra, “Alternative Propulsion in Railway Passenger Transport - The Way to Green Transport,” Transp. Res. Procedia, vol. 77, no. 2023, pp. 43–50, 2024.

I. Riabov, S. Goolak, L. Kondratieva, and L. Overianova, “Increasing the energy efficiency of the multi-motor traction electric drive of an electric locomotive for railway quarry transport,” Eng. Sci. Technol. an Int. J., vol. 42, p. 101416, 2023.

M. Z. Romdlony and F. Irsyadi, “Hardware-in-the-loop simulation of DC motor as an instructional media for control system design and testing,” J. Mechatronics, Electr. Power, Veh. Technol., vol. 12, no. 2, pp. 81–86, 2021.

S. Nategh et al., “A review on different aspects of traction motor design for railway applications,” IEEE Trans. Ind. Appl., vol. 56, no. 3, pp. 2148–2157, 2020.

Sang-Hoon Kim, Electric Motor Control: DC, AC, and BLDC Motors. Amsterdam, Netherlands: Elsevier, 2017. [Online].

B. D. Hughes Austin, Electric Motors and Drives, 5th ed., vol. 11, no. 1. The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States: Elsevier Ltd., 2019.

Menteri Perhubungan Republik Indonesia, “Peraturan Menteri Perhubungan Nomor 60 Tahun 2012 tentang Persyaratan Teknis Jalur Kereta Api,” Kementrian Perhub. Republik Indones., pp. 1–57, 2012, [Online].

PT INKA, “Train Performance,” MADIUN, 2020. [Online].

C. Upendra Reddy, K. K. Prabhakar, A. K. Singh, and P. Kumar, “Speed estimation technique using modified stator current error-based MRAS for direct torque controlled induction motor drives,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 8, no. 2, pp. 1223–1235, 2020.

B. R. Vinod and G. Shiny, “Direct torque control scheme for a four-level-inverter fed open-end-winding induction motor,” IEEE Trans. Energy Convers., vol. 34, no. 4, pp. 2209–2217, 2019.

X. Wu, W. Huang, X. Lin, W. Jiang, Y. Zhao, and S. Zhu, “Direct torque control for induction motors based on minimum voltage vector error,” IEEE Trans. Ind. Electron., vol. 68, no. 5, pp. 3794–3804, 2021.

R. K. Bindal and I. Kaur, “Torque ripple reduction of Induction Motor using Dynamic Fuzzy Prediction Direct Torque Control,” ISA Trans., vol. 99, pp. 322–338, 2020.

Y. Qu, B. Zhang, H. Chu, H. Shen, J. Zhang, and X. Yang, “Sliding-mode anti-disturbance speed control of permanent magnet synchronous motor based on an advanced reaching law,” ISA Trans., vol. 139, pp. 436–447, 2023.

D. Zellouma, Y. Bekakra, and H. Benbouhenni, “Field-oriented control based on parallel proportional–integral controllers of induction motor drive,” Energy Reports, vol. 9, pp. 4846–4860, 2023.

H.-G. H. Mirza Abdul Waris Begh, “Comparison of field oriented controland direct torque control,” TechRxiv, pp. 1–16, 2018.

A. P. Widyadharma et al., “Close loop speed control design for 3ø induction motor on electric railway with embedded PID controller,” Int. Res. J. Adv. Eng. Sci., vol. 7, no. 3, pp. 210–213, 2022. [Online].

S. Chandrasekaran, S. Durairaj, and S. Padmavathi, “A performance evaluation of a fuzzy logic controller-based photovoltaic-fed multi-level inverter for a three-phase induction motor,” J. Franklin Inst., vol. 358, no. 15, pp. 7394–7412, 2021.

Z. M. S. Elbarbary, O. K. Al-Harbi, S. F. Al-Gahtani, S. M. Irshad, A. Y. Abdelaziz, and M. A. Mossa, “Review of speed estimation algorithms for three- phase induction motor,” MethodsX, vol. 12, no. August 2023, p. 102546, 2024.

I. Anshory et al., “Optimization DC-DC boost converter of BLDC motor drive by solar panel using PID and firefly algorithm,” Results Eng., vol. 21, no. December 2023, p. 101727, 2024.

E. saadi Terfia, S. Mendaci, S. E. Rezgui, H. Gasmi, and W. Kantas, “ Optimal third-order sliding mode controller for dual star induction motor based on grey wolf optimization algorithm,” Heliyon, vol. 10, no. 12, p. e32669, 2024.

I. I. Novendra, I. M. Wirawan, A. Kusumawardana, and A. K. Latt,“ Optimization of load frequency control using grey wolf optimizer in micro hydro power plants,” J. Mechatronics, Electr. Power, Veh. Technol., vol. 14, no. 2, pp. 166–176, 2023.

H. A. Toliyat and S. G. Campbell, DSP-Based Electromechanical Motion Control. Boca Raton, FL: CRC Press, 2004. [Online]. Available: https://doi.org/10.1201/9780203486337

P. C. Krause, O. Wasynczuk, S. D. Sudhoff, and S. D. Pekarek, Analysis of Electric Machinery and Drive Systems, 4th ed. Hoboken, NJ: Wiley IEEE Press (John Wiley & Sons), 2025.

A. Almabrok, M. Psarakis, and A. Dounis, “Fast tuning of the PID controller in an HVAC system using the Big Bang-Big Crunch algorithm and FPGA technology,” Algorithms, vol. 11, no. 10, 2018.

W. A. Khan, N. N. Hamadneh, S. L. Tilahun, and J. M. T. Ngnotchouye, “A review and comparative study of firefly algorithm and its modified versions,” Optim. Algorithms - Methods Appl., 2016.

M. S. Shaikh, C. Hua, M. A. Jatoi, M. M. Ansari, and A. A. Qader, “Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system,” IET Sci. Meas. Technol., vol. 15, no. 2, pp. 218–231, 2021.

D. Awasthi, A. Tiwari, P. Khare, and V. K. Srivastava, “A comprehensive review on optimization-based image watermarking techniques for copyright protection,” Expert Syst. Appl., vol. 242, no. 122830, 2024.