Adaptive PID Controller Design for Velocity Control of a Hydrogen Internal Combustion Engines using RBF Neural Networks

Quang Truc Dam; Fatima Haidar

doi:10.29228/eng.pers.76280

Adaptive PID Controller Design for Velocity Control of a Hydrogen Internal Combustion Engines using RBF Neural Networks

Abstract

Achieving precise velocity control in ICEs is crucial for optimizing performance, fuel efficiency, and reducing emissions. However, the nonlinear dynamics and uncertainties inherent in ICE systems pose significant challenges for conventional control methods. In this paper, we propose an adaptive approach integrating a Proportional-Integral-Derivative (PID) controller with Radial Basis Function Neural Networks (RBFNN) to address these challenges effectively. The proposed controller architecture comprises two main components: a RBFNN designed to estimate modeling uncertainties, such as unknown friction and external disturbances impacting the ICE structure, and a primary PID controller responsible for regulating velocity. The RBFNN serves as a dynamic estimator, continuously learning and adapting to variations in system dynamics, thereby enhancing the controller's robustness and adaptability. By accurately capturing the nonlinearities and uncertainties inherent in ICEs, the RBFNN contributes to improved control performance and stability. To validate the efficacy of the proposed approach, extensive numerical simulations are conducted using MATLAB Simulink. The simulations involve various operating conditions and scenarios to comprehensively evaluate the controller's performance. Additionally, the proposed methodology is compared against conventional PID methods documented in the literature to assess its superiority in terms of robustness, tracking accuracy, and disturbance rejection. The results demonstrate that the adaptive PID controller utilizing RBFNNs outperforms traditional PID approaches, exhibiting superior velocity regulation and disturbance rejection capabilities. Moreover, the proposed methodology showcases promising potential for real-world implementation in ICE-based systems, offering enhanced control performance and efficiency. Overall, this study contributes to advancing the field of control engineering by introducing a novel adaptive control strategy tailored specifically for velocity control in internal combustion engines, leveraging the capabilities of RBFNNs to mitigate uncertainties and improve overall system performance.

Keywords

References

1. Ehsani, M., Gao, Y., Longo, S., & Ebrahimi, K. (2018). Modern electric, hybrid electric, and fuel cell vehicles (3rd ed.). CRC Press.
2. Stone, R. (1992). Introduction to internal combustion engines (2nd ed.). The Macmillan Press LTD.
3. Heywood, J. B. (2018). Internal combustion engine fundamentals (2nd ed.). McGraw-Hill Education.
4. Nise, N. S. (2015). Control systems engineering (7th ed.). Wiley.
5. Gao, D., Lei, Y., Zhu, H., & Ni, J. (2015). Constant speed control of four-stroke micro internal combustion swing engine. Chinese Journal of Mechanical Engineering, 28(5), 971–982. https://doi.org/10.3901/CJME.2015.0512.070
6. López, J. D., Espinosa, J. J., & Agudelo, J. R. (2011). LQR control for speed and torque of internal combustion engines. IFAC Proceedings Volumes, 44(1), 2230–2235. https://doi.org/10.3182/20110828-6-IT-1002.02176
7. Norouzi, A., Heidarifar, H., Shahbakhti, M., Koch, C. R., & Borhan, H. (2021). Model predictive control of internal combustion engines: A review and future directions. Energies, 14(19), 6251. https://doi.org/10.3390/en14196251
8. Gordon, D. C., et al. (2022). End-to-end deep neural network based nonlinear model predictive control: Experimental implementation on diesel engine emission control. Energies, 15(24), 9335. https://doi.org/10.3390/en15249335

9. Di Cairano, S., Doering, J., Kolmanovsky, I. V., & Hrovat, D. (2014). Model predictive control of engine speed during vehicle deceleration. IEEE Transactions on Control Systems Technology, 22(6), 2205–2217. https://doi.org/10.1109/TCST.2014.2309671
10. Norouzi, A., Heidarifar, H., Shahbakhti, M., Koch, C. R., & Borhan, H. (2021). Model predictive control of internal combustion engines: A review and future directions. Energies, 14(19), 6251. https://doi.org/10.3390/en14196251
11. Souder, J. S., & Hedrick, J. K. (2004). Adaptive sliding mode control of air–fuel ratio in internal combustion engines. International Journal of Robust & Nonlinear Control, 14(6), 525–541. https://doi.org/10.1002/rnc.901
12. Norouzi, A., et al. (2022). Deep learning-based model predictive control for compression ignition engines. arXiv preprint. https://doi.org/10.48550/ARXIV.2204.00139
13. Norouzi, A., Shahpouri, S., Gordon, D., Shahbakhti, M., & Koch, C. R. (2023). Safe deep reinforcement learning in diesel engine emission control. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 237(8), 1440–1453. https://doi.org/10.1177/09596518231153445
14. Heywood, J. B. (1988). Internal combustion engine fundamentals. McGraw-Hill.
15. Dam, Q. T., Thabet, R. E. H., Ali, S. A., Guerin, F., & Tang, Y. (2023, September 27). Adaptive neural network-based sliding mode controller for trajectory tracking of a quadrotor UAV. TechRxiv. https://doi.org/10.36227/techrxiv.24174771.v1
16. Dam, Q. T., Thabet, R. E. H., Ali, S. A., Guérin, F., & Ghani, H. A. (2023). A high-gain observer design for nonlinear system with delayed measurements: Application to a quadrotor UAV. IFAC-PapersOnLine, 56(2), 6739–6744. https://doi.org/10.1016/j.ifacol.2023.10.379
17. Dam, Q. T., Thabet, R. E. H., Ali, S. A., & Guerin, F. (2024). Observer design for a class of uncertain nonlinear systems with sampled-delayed output using high-gain observer and low-pass filter: Application for a quadrotor UAV. IEEE Transactions on Industrial Electronics, 71(1), 933–942. https://doi.org/10.1109/TIE.2023.3247786
18. Dam, Q. T., Thabet, R. E. H., Ali, S. A., & Guerin, F. (2022, June). Filtered high-gain observer design for a class of nonlinear systems subject to delayed measurements: Application to a quadrotor UAV. 2022 American Control Conference (ACC), Atlanta, GA, USA. IEEE. https://doi.org/10.23919/ACC53348.2022.9867537
19. Dam, Q. T., Thabet, R. E. H., Ali, S. A., Guérin, F., & Hugo, A. (2021, June). Continuous–discrete time high gain observer design for state and unknown inputs estimations of quadrotor UAV. 2021 European Control Conference (ECC), Delft, Netherlands. IEEE. https://doi.org/10.23919/ECC54610.2021.9655232
20. Dam, Q. T., Haidar, F., Mama, N., & Chennapalli, S. J. (2024). Modeling and simulation of an internal combustion engine using hydrogen: A MATLAB implementation approach. Engineering Perspective, 4(3), 108–118. http://dx.doi.org/10.29228/eng.pers.76219

Details

Primary Language

English

Subjects

Automotive Mechatronics and Autonomous Systems

Journal Section

Research Article

Authors

Quang Truc Dam This is me
France

Fatima Haidar This is me
France

Publication Date

March 31, 2025

Submission Date

April 19, 2024

Acceptance Date

January 13, 2025

Published in Issue

Year 2025 Volume: 5 Number: 1

DOI

https://doi.org/10.29228/eng.pers.76280

IZ

https://izlik.org/JA23WG57PR

Cite

RIS / Bibtex

APA

Dam, Q. T., & Haidar, F. (2025). Adaptive PID Controller Design for Velocity Control of a Hydrogen Internal Combustion Engines using RBF Neural Networks. Engineering Perspective, 5(1), 41-48. https://doi.org/10.29228/eng.pers.76280

AMA

1.Dam QT, Haidar F. Adaptive PID Controller Design for Velocity Control of a Hydrogen Internal Combustion Engines using RBF Neural Networks. engineeringperspective. 2025;5(1):41-48. doi:10.29228/eng.pers.76280

Chicago

Dam, Quang Truc, and Fatima Haidar. 2025. “Adaptive PID Controller Design for Velocity Control of a Hydrogen Internal Combustion Engines Using RBF Neural Networks”. Engineering Perspective 5 (1): 41-48. https://doi.org/10.29228/eng.pers.76280.

EndNote

Dam QT, Haidar F (March 1, 2025) Adaptive PID Controller Design for Velocity Control of a Hydrogen Internal Combustion Engines using RBF Neural Networks. Engineering Perspective 5 1 41–48.

IEEE

[1]Q. T. Dam and F. Haidar, “Adaptive PID Controller Design for Velocity Control of a Hydrogen Internal Combustion Engines using RBF Neural Networks”, engineeringperspective, vol. 5, no. 1, pp. 41–48, Mar. 2025, doi: 10.29228/eng.pers.76280.

ISNAD

Dam, Quang Truc - Haidar, Fatima. “Adaptive PID Controller Design for Velocity Control of a Hydrogen Internal Combustion Engines Using RBF Neural Networks”. Engineering Perspective 5/1 (March 1, 2025): 41-48. https://doi.org/10.29228/eng.pers.76280.

JAMA

1.Dam QT, Haidar F. Adaptive PID Controller Design for Velocity Control of a Hydrogen Internal Combustion Engines using RBF Neural Networks. engineeringperspective. 2025;5:41–48.

MLA

Dam, Quang Truc, and Fatima Haidar. “Adaptive PID Controller Design for Velocity Control of a Hydrogen Internal Combustion Engines Using RBF Neural Networks”. Engineering Perspective, vol. 5, no. 1, Mar. 2025, pp. 41-48, doi:10.29228/eng.pers.76280.

Vancouver

1.Quang Truc Dam, Fatima Haidar. Adaptive PID Controller Design for Velocity Control of a Hydrogen Internal Combustion Engines using RBF Neural Networks. engineeringperspective. 2025 Mar. 1;5(1):41-8. doi:10.29228/eng.pers.76280

Cited By

A novel approach to extending the operating range of a low temperature combustion engine using variable lug offset with hypocycloid gear mechanism

Energy Conversion and Management

https://doi.org/10.1016/j.enconman.2025.120424

Leveraging Advanced Machine Learning Algorithms for Energy Efficiency and Carbon Footprint Reduction in Diesel Engines

International Journal of Automotive Science And Technology

https://doi.org/10.30939/ijastech..1896122

Development and characterization of sulfonated poly(sulfone)/poly(styrene-co-acrylonitrile) composite blend membranes for enhanced proton conductivity

Process Safety and Environmental Protection

https://doi.org/10.1016/j.psep.2025.107607