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

Year 2025, Volume: 5 Issue: 1, 41 - 48, 31.03.2025

Quang Truc Dam Fatima Haidar

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

https://izlik.org/JA23WG57PR

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

Internal Combustion Engine (ICE) , MATLAB-Simulink , Proportional-Integral-Derivative (PID) , Radial Basis Function Neural Networks (RBFNN) , Hydrogen

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