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Comparative Optimal Tuning of a PID Controller for a SEDC Motor Using MCTA and TLBO

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Abstract:

A Separately Excited DC (SEDC) motor is widely used in process industries and automotive applications because of its fast response and high reliability. This paper presents the optimal tuning of a PID controller for an SEDC motor using two nature-inspired optimizers: the Modified Camel Traveling Algorithm (MCTA) and the Teaching-Learning-Based Optimization (TLBO). Each algorithm is applied independently to minimize the Integral of Time-Weighted Absolute Error (ITAE) in the MATLAB/Simulink environment, while a conventional trial-and-error PID serves as the baseline. Controller performance is evaluated using convergence profiles and time-domain indices (rise time, settling time, and overshoot) under reference changes and load disturbances. Both optimizers improve the transient response compared with the baseline; across all tests, TLBO achieves the lowest ITAE and slightly shorter rise and settling times, whereas MCTA remains competitive. The findings provide clear comparative insights and practical guidance for selecting between TLBO and MCTA in SEDC speed control applications.

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Periodical:

Applied Mechanics and Materials (Volume 934)

Pages:

47-62

DOI:

https://doi.org/10.4028/p-wB9WS0

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Cite this paper

Online since:

February 2026

Authors:

Zahraa S. Salim*, Diyah K. Shary, Hayder D. Almukhtar

Keywords:

Modified Camel Traveling Algorithm, PID Controller, Separately Excited DC Motor, Teaching-Learning-Based Optimization

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© 2026 Trans Tech Publications Ltd. All Rights Reserved

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* - Corresponding Author

References

[1] M. Jabari, S. Ekinci, D. Izci, M. Bajaj and I. Zaitsev, Efficient DC motor speed control using a novel multi-stage FOPD(1 + PI) controller optimized by the Pelican optimization algorithm, Sci. Rep. 14 (2024) 22442.

DOI: 10.1038/s41598-024-73409-5

Google Scholar

[2] A.F. Güven, Ö.Ö. Mengi, M.A. Elseify and S. Kamel, Comprehensive optimization of PID controller parameters for DC motor speed management using a modified jellyfish search algorithm, Optim. Control Appl. Methods 46 (2025) 320–342.

DOI: 10.1002/oca.3218

Google Scholar

[3] Y.B. Koca, Y. Aslan and B. Gökçe, Speed control based PID configuration of a DC motor for an unmanned agricultural vehicle, in: Proc. 8th Int. Conf. Electr. Electron. Eng. (ICEEE) (2021) 117–120.

DOI: 10.1109/iceee52452.2021.9415908

Google Scholar

[4] X. Yang, W. Ge and Y. Wang, Design and application of parameter self-tuning regulator for DC motor based on neural network, Scalable Comput.: Pract. Exp. 25 (2024) 4825–4835.

DOI: 10.12694/scpe.v25i6.3301

Google Scholar

[5] M. Munadi, M.A. Akbar, T. Naniwa and Y. Taniai, Model reference adaptive control for DC motor based on Simulink, in: Proc. 6th Int. Annu. Eng. Seminar (InAES) (2016) 1–6.

DOI: 10.1109/inaes.2016.7821915

Google Scholar

[6] Y. Guo and M.E.A. Mohamed, Speed control of direct current motor using ANFIS based hybrid P-I-D configuration controller, IEEE Access 8 (2020) 125638–125647.

DOI: 10.1109/access.2020.3007615

Google Scholar

[7] B. Hekimoğlu, Optimal tuning of fractional order PID controller for DC motor speed control via chaotic atom search optimization algorithm, IEEE Access 7 (2019) 38100–38114.

DOI: 10.1109/access.2019.2905961

Google Scholar

[8] S.B. Joseph, E.G. Dada, A. Abidemi, D.O. Oyewola and B.M. Khammas, Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems, Heliyon 8 (2022) e09399.

DOI: 10.1016/j.heliyon.2022.e09399

Google Scholar

[9] O. Rodríguez-Abreo, J. Rodríguez-Reséndiz, C. Fuentes-Silva, R. Hernández-Alvarado and M.D.C.P. Torres Falcón, Self-tuning neural network PID with dynamic response control, IEEE Access 9 (2021) 65206–65215.

DOI: 10.1109/access.2021.3075452

Google Scholar

[10] M.M. Gani, M.S. Islam and M.A. Ullah, Modeling and designing a genetically optimized PID controller for separately excited DC motor, in: Proc. Int. Conf. Electr., Comput. Commun. Eng. (ECCE) (2019) 1–6.

DOI: 10.1109/ecace.2019.8679276

Google Scholar

[11] A.I. Tajudin, M.A.D. Izani, A.A.A. Samat, S. Omar and M.A.M. Idin, Design a speed control for DC motor using an optimal PID controller implementation of ABC algorithm, in: Proc. IEEE Int. Conf. Control Syst. Comput. Eng. (ICCSCE) (2022) 97–102.

DOI: 10.1109/iccsce54767.2022.9935644

Google Scholar

[12] R.S. Patil, S.P. Jadhav and M.D. Patil, Review of intelligent and nature-inspired algorithms-based methods for tuning PID controllers in industrial applications, J. Robot. Control 5 (2024) 336–350.

DOI: 10.18196/jrc.v5i2.20850

Google Scholar

[13] W. Aribowo, Supari and B. Suprianto, Optimization of PID parameters for controlling DC motor based on the Aquila optimizer algorithm, Int. J. Power Electron. Drive Syst. 13 (2022) 216–222.

DOI: 10.11591/ijpeds.v13.i1.pp216-222

Google Scholar

[14] B.B. Acharya, S. Dhakal, A. Bhattarai and N. Bhattarai, PID speed control of DC motor using meta-heuristic algorithms, Int. J. Power Electron. Drive Syst. 12 (2021) 822–831.

DOI: 10.11591/ijpeds.v12.i2.pp822-831

Google Scholar

[15] R.S.A. Ali and M.K. Ibrahim, Novel optimization algorithm inspired by camel traveling behavior, Iraq J. Electr. Electron. Eng. 12 (2016) 167–177.

DOI: 10.33762/eeej.2016.118375

Google Scholar

[16] M.F. Demiral, Heuristics in labor management: An application of modified camel algorithm, Pamukkale J. Eurasian Socioecon. Stud. 11 (2024) 37–48.

DOI: 10.34232/pjess.1498652

Google Scholar

[17] R.S. Ali, F.M. Alnahwi and A.S. Abdullah, A modified camel travelling behaviour algorithm for engineering applications, Aust. J. Electr. Electron. Eng. 17 (2019) 233–246.

Google Scholar

[18] K.M. Omran, B.H. Jasim and K.H. Hassan, Optimum speed controller structure utilizing the MCA approach, Bull. Electr. Eng. Inform. 10 (2021) 640–649.

DOI: 10.11591/eei.v10i2.2733

Google Scholar

[19] S.K. Pandey, C. Bera and S.S. Dwivedi, Design of robust PID controller for DC Motor using TLBO algorithm, in: Proc. IEEE Int. Conf. Adv. Dev. Electr. Electron. Eng. (ICADEE) (2020) 1–6.

DOI: 10.1109/icadee51157.2020.9368952

Google Scholar

[20] R. Nutenki and B.V. Varma, PID controller design for DC motor speed control, in: Proc. 4th Int. Conf. Adv. Electr., Comput., Commun. Sustain. Technol. (ICAECT) (2024) 1–6.

DOI: 10.1109/icaect60202.2024.10469124

Google Scholar

[21] H. Supriyono, F.F. Alanro and A. Supardi, Development of DC motor speed control using PID based on Arduino and Matlab for laboratory trainer, J. Nas. Tek. Elektro 13 (2024) 37–41.

DOI: 10.25077/jnte.v13n1.1155.2024

Google Scholar

[22] R. Krishnan, Electric Motor Drives: Modeling, Analysis and Control, Prentice Hall, 2001.

Google Scholar

[23] S.S. Sami, Z.A. Obaid, M.T. Muhssin and A.N. Hussain, Detailed modelling and simulation of different DC motor types for research and educational purposes, Int. J. Power Electron. Drive Syst. 12 (2021) 703–714.

DOI: 10.11591/ijpeds.v12.i2.pp703-714

Google Scholar

[24] Q. Ariyansyah and A. Ma'arif, DC motor speed control with proportional integral derivative (PID) control on the prototype of a mini-submarine, J. Fuzzy Syst. Control 1 (2023) 18–24.

DOI: 10.59247/jfsc.v1i1.26

Google Scholar

[25] S.A. Aessa, S.W. Shneen and M.K. Oudah, Optimizing PID controller for large-scale MIMO systems using flower pollination algorithm, J. Robot. Control 6 (2025) 553–559.

DOI: 10.18196/jrc.v6i2.24409

Google Scholar

[26] S.M. Dawood, S.H. Majeed and H.J. Nekad, PID controller based multiple (master/slaves) permanent magnet synchronous motors speed control, Iraq J. Electr. Electron. Eng. 11 (2015) 22.

DOI: 10.37917/ijeee.11.2.5

Google Scholar

[27] R.P. Borase, D.K. Maghade, S.Y. Sondkar and S.N. Pawar, A review of PID control, tuning methods and applications, Int. J. Dyn. Control 10 (2022) 1041–1062.

DOI: 10.1007/s40435-020-00665-4

Google Scholar

[28] A.K. Naik, S.K. Kar and B.K. Sahu, Speed control of DC motor using linear and non-linear controllers, in: Proc. 1st Odisha Int. Conf. Electr. Power Eng., Commun. Comput. Technol. (ODICON) (2021) 1–6.

DOI: 10.1109/odicon50556.2021.9428996

Google Scholar

[29] H.J. Nekad, D.K. Shary and M.A. Alawan, Position control of linear synchronous reluctance motor using a modified camel traveling algorithm-based proportional integral controller, Math. Model. Eng. Probl. 11 (2024) 1585–1592.

DOI: 10.18280/mmep.110619

Google Scholar

[30] I. Husnaini, K. Krismadinata, A. Asnil and H. Hastuti, PI and PID controller design and analysis for DC shunt motor speed control, Int. J. Recent Technol. Eng. 8 (2019) 144–150.

DOI: 10.35940/ijrte.c6521.118419

Google Scholar

[31] J. Kennedy and R. Eberhart, Particle swarm optimization, in: Proc. IEEE Int. Conf. Neural Networks (ICNN) (1995) 1942–1948.

DOI: 10.1109/icnn.1995.488968

Google Scholar

[32] A. Askarzadeh, A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm, Comput. Struct. 169 (2016) 1–12.

DOI: 10.1016/j.compstruc.2016.03.001

Google Scholar

[33] R.S. Ali, J.R. Mahmood and H.M. Badr, A new version of modified camel algorithm for engineering applications, in: Proc. Int. Conf. Design, Innovation and Sustainable Technologies (IMDC-IST) (2021).

DOI: 10.4108/eai.7-9-2021.2314874

Google Scholar

[34] R.V. Rao, V.J. Savsani and D.P. Vakharia, Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems, Comput.-Aided Des. 43 (2011) 303–315.

DOI: 10.1016/j.cad.2010.12.015

Google Scholar

[35] D. Wu, S. Wang, Q. Liu, L. Abualigah and H. Jia, An improved teaching-learning-based optimization algorithm with reinforcement learning strategy for solving optimization problems, Comput. Intell. Neurosci. 2022 (2022) Article ID 1535957.

DOI: 10.1155/2022/1535957

Google Scholar

[36] D. Qu, S. Liu, D. Zhang, J. Wang and C. Gao, Teaching-learning based optimization algorithm based on course by course improvement, in: Proc. 11th Int. Conf. Comput. Intell. Secur. (CIS) (2015) 48–52.

DOI: 10.1109/cis.2015.20

Google Scholar

[37] N.L. Manuel, N. İnanç and M. Lüy, Control and performance analyses of a DC motor using optimized PIDs and fuzzy logic controller, Results Control Optim. 13 (2023) 100306.

DOI: 10.1016/j.rico.2023.100306

Google Scholar

[38] S. Ekinci, B. Hekimoğlu and D. Izci, Opposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motor, Eng. Sci. Technol. Int. J. 24 (2021) 331–342.

DOI: 10.1016/j.jestch.2020.08.011

Google Scholar

[39] I. Khanam and G. Parmar, Application of SFS algorithm in control of DC motor and comparative analysis, in: Proc. 4th IEEE Uttar Pradesh Sect. Int. Conf. Electr. Comput. Electron. (UPCON) (2017) 256–261.

DOI: 10.1109/upcon.2017.8251057

Google Scholar

[40] S. Ekinci, B. Hekimoğlu, A. Demiroren and E. Eker, Speed control of DC motor using improved sine cosine algorithm based PID controller, in: Proc. Int. Symp. Multidiscip. Stud. Innov. Technol. (ISMSIT) (2019) 1–5.

DOI: 10.1109/ismsit.2019.8932907

Google Scholar