Abstract
Gas turbine is a kind of dual-purpose rotating thermal machinery, widely used in power generation, shipbuilding and aviation power, etc., with the advantages of high efficiency, fast start and low emissions. In this study, an improved Circle-SCA-BSO algorithm (IC-SCA-BSO) is proposed to optimize PID parameters to ease the complicated parameter setting of gas turbine controller. Optimization of beetle swarm optimization algorithm (BSO) usually comes with slow convergence speed, low accuracy and prone to fall into local optimum, hereby our optimization is carried out from three aspects: population initialization, optimization weight and learning factors. First, a uniformly distributed circle mapping is utilized for population initialization. Second, the nonlinear decreasing idea is employed for weight optimization. Considering characteristics of global search in the early stage and local development in the later stage of algorithm optimization, the nonlinear decreasing function expression is designed. Third, combined with the sine cosine algorithm (SCA), the sine and cosine factors are introduced into the learning factors and combined with the nonlinear decreasing coefficient to make the learning factors show a trend of oscillatory attenuation in the set interval. According to probability p, sine or cosine factor is switched as the learning factors. The optimized PID controller and other four controllers are compared by tracking test and anti-interference test. The test results show that IC-SCA-BSO-PID yields faster response, higher steady-state accuracy and stronger anti-interference control effect, which is significantly outperforming the other four controllers. The proposed IC-SCA-BSO-PID tuning framework offers plant engineers an automated, low-cost alternative to labor-intensive manual calibration, enabling faster commissioning, reduced fuel consumption, and lower emissions for gas turbines in power-generation, marine, and aero-derivative applications.
Data availability
The authors declare that the data supporting the findings of this study are available within the paper and the Zenodo repository (https://doi.org/10.5281/zenodo.17838838). All experimental data can be found in the “results” directory of the repository. For any additional inquiries regarding the data, please contact the corresponding author (wangzixiao@cuc.edu.cn).
Code availability
The custom code used in this study is publicly available on Zenodo at https://doi.org/10.5281/zenodo.17838838.
References
Eslami, M. & Babazadeh, M. A unified acceptance test framework for power plant gas turbine control systems. J. ISA Trans. 85, 262–273 (2019).
Chakraborty, S., Mondal, A. & Biswas, S. Design of Type-2 fuzzy controller for hybrid Multi‐Area power system. J Fuzzy Log. Appl. Comput. Sci. Mathematics 107–124 (2023).
Wang, L. PID Control System Design and Automatic Tuning Using MATLAB/Simulink Vol. 259 (Wiley, 2020).
Chakraborty, S. & Mondal, A. Design of FUZZY-(1 + PD)‐FOPID controller for hybrid Two‐Area power system. J Controller Des. Industrial Applications 105–124 (2025).
Chakraborty, S., Mondal, A. & Biswas, S. Application of FUZZY-3DOF-PID controller for controlling FOPTD type communication delay based renewable three-area deregulated hybrid power system. J. Evolutionary Intell. 17 (4), 2821–2841 (2024).
Chakraborty, S., Mondal, A. & Das, C. Fuzzy fractional order PID controller design for AVR system. In 2024 IEEE 3rd International Conference on Control, Instrumentation, Energy & Communication (CIEC) (pp. 31–36). IEEE (2024).
Chakraborty, S. & Mondal, A. TSA-aided IT2FLC-(1 + PD)-FOPID control for regulating the first-order plus time-delayed non-conventional multi-area power system with deregulation. J Electr. Engineering 1–21 (2025).
Das, N. & Sengupta, A. A comparison between adaptive pid controller and pid controller with derivative path filter based on bacterial foraging optimization algorithm. In 2018 2nd International Conference on Power, Energy and Environment: Towards Smart Technology (ICEPE) (pp. 1–6). IEEE (2018).
Gupta, S., Bansal, P., Gupta, R. & Mewara, M. Optimal Tuning of PID Controller Parameters for HVAC System Using BF-PSO Algorithm. In 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON) (pp. 1–5). IEEE (2023).
Lin, X., Liu, Y. & Wang, Y. Design and research of DC motor speed control system based on improved BAS. In 2018 Chinese Automation Congress (CAC) (pp. 3701–3705). IEEE (2018).
Wang, L., Liu, J., Wu, Q. & Wang, X. Ship course control based on BSO-PID online self-optimization algorithm. In 2019 5th International Conference on Transportation Information and Safety (ICTIS) (pp. 1405–1411). IEEE (2019).
Sun, J., Wu, L. & Yang, X. Optimal Fractional Order PID Controller Design for AVR System Based on Improved Genetic Algorithm. In 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) (pp. 351–355). IEEE (2020).
You, M., Wu, Y., Wang, Y., Xie, X. & Xu, C. Parameter optimization of PID controller based on improved sine-SOA algorithm. In 2022 IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 12–17). IEEE (2022).
Guo, S., He, A., Xiao, B., Liu, P. & Wang, Z. A novel integrated control method for an aero-derivative gas turbine of power generation. J. Machines. 10 (3), 179 (2022).
Patil, S. R. & Agashe, S. D. Auto tuned PID and neural network predictive controller for a flow loop pilot plant. J. Mater. Today: Proc. 72, 754–760 (2023).
Singh, R. K., Verma, V. & ANN-Tuned, P. I. D. Controller for LFC Investigation in Two-Area Interconnected System. In 2023 International Conference on Power, Instrumentation, Energy and Control (PIECON) (pp. 1–5). IEEE (2023).
Xie, H., Tang, H. & Liao, Y. H. Time series prediction based on NARX neural networks: An advanced approach. In 2009 International conference on machine learning and cybernetics (Vol. 3, pp. 1275–1279). IEEE (2009).
Schmidhuber, J. Deep learning in neural networks: an overview. J. Neural Networks. 61, 85–117 (2015).
Mirjalili, S. SCA: a sine cosine algorithm for solving optimization problems. J. Knowledge-based Syst. 96, 120–133 (2016).
Wang, T. & Yang, L. Beetle swarm optimization algorithm:Theory and application. Preprint at (2018). https://arxiv.org/abs/1808.00206
Arora, S. & Anand, P. Chaotic grasshopper optimization algorithm for global optimization. J. Neural Comput. Appl. 31, 4385–4405 (2019).
Amitava, G., Indranil, P., Saptarshi, D. & Shantanu, D. A novel fractional order fuzzy PID controller and its optimal time domain tuning based on integral performance indices. J. Eng. Appl. Artif. Intell. 25 (2), 430–442 (2012).
Hermanu, C., Ibrahim, M. H., Saputro, J. S., Maghfiroh, H. & Sujono, A. Performance evaluation of different objective function in PID tuned by PSO in DC-motor speed control. In IOP Conference Series: Materials Science and Engineering. 1096(1): 012061. IOP Publishing (2021).
Sudholt, D. Theory of swarm intelligence. In Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO Comp) (pp. 687–708). ACM (2014).
Priyadarshi, R. & Kumar, R. R. Evolution of swarm intelligence: a systematic review of particle swarm and ant colony optimization approaches in modern research. J. Arch. Comput. Methods Eng. 32, 3609–3650 (2025).
Tarique, A. & Gabbar, H. A. Particle swarm optimization (PSO) based turbine control. J. Intell. Control Autom. 4 (2), 126–137 (2013).
Mohamed, O. & Khalil, A. Progress in modeling and control of gas turbine power generation systems: a survey. J. Energies. 13 (9), 2358 (2020).
So, G. B. A novel CEM-based 2-DOF PID controller for low-pressure turbine speed control of marine gas turbine engines. J. Processes. 12 (9), 1916 (2024).
Codes, P. T. Gas Turbines. (2014). http://musco.ir/wp-content/uploads/2020/05/ASME-PTC-22.pdf
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The authors acknowledge the contributions of all co-authors who participated in the experimental design, data processing, analysis, and manuscript preparation.
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Professor Yue Dong conceived the experiments. Xiaona Liu was responsible for revising, checking, correcting, and drawing images of the manuscript, and made detailed adjustments to the article based on the reviewer’s comments. Zixiao Wang and professor Lina Zhang conducted the experiments, Professor Yue Dong and Dr. Xinxiang Zhang analyzed the results. Dr. Xinxiang Zhang organized the modification of the report. All authors reviewed the manuscript.
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Dong, Y., Liu, X., Wang, Z. et al. Improved circle-SCA-BSO optimized gas turbine speed PID controller for enhanced speed tracking and interference rejection. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37087-9
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DOI: https://doi.org/10.1038/s41598-026-37087-9