Closed-Loop System Identification and Gain Scheduled Control of Piston Engine–Dynamometer System with Disturbance Observer
Source abstract
Abstract
Piston engine system identification models are crucial for autonomous and control-oriented applications. However, piston engines exhibit relatively unstable behavior, which may lead to overspeed during open-loop operation. In this study, system identification of a piston engine is investigated on a piston engine–dynamometer test bench. The identification process is carried out under closed-loop conditions to ensure safe and reliable operation. Known system parameters are utilized to transform the closed-loop system into an equivalent open-loop representation. Three system identification models—a State-Space model, a nonlinear ARX model, and a Hammerstein–Wiener model—are employed and comparatively evaluated. The identified models are validated based on their closed-loop responses. Subsequently, a gain scheduled cruise controller is designed using the identified model and implemented on an engine dynamic model. Finally, a disturbance observer is integrated into the cruise controller to enhance disturbance rejection performance. The disturbance observer is developed based on the identified system model.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Control Systems in Engineering · Control Systems and Identification · Vehicle Noise and Vibration Control
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Mechatronics Engineering, Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, İstanbul 34956, Türkiye
References (41)
Show source-provided references
1. Modeling of two-stroke aviation piston engines for control applications10.1177/16878132231153234 ↗
2. A survey on modeling, biofuels, control and supervision systems applied in internal combustion engines10.1016/j.rser.2017.01.168 ↗
3. Crossley, P.R., and Cook, J.A. (1991). A nonlinear engine model for drivetrain system development. International Conference on Control, IET.
4. Zhihu, W., and Run, P. (2008). Torque based spark ignition engine and powertrain modeling. 7th World Congress on Intelligent Control and Automation, IEEE.10.1109/WCICA.2008.4593695 ↗
5. Neural-networks-based nonlinear dynamic modeling for automotive engines10.1016/S0925-2312(99)00121-6 ↗
6. Diesel engine modeling based on recurrent neural networks for a hardware-in-the-loop simulation system of diesel generator sets10.1016/j.neucom.2017.12.054 ↗
7. Modelling of automotive engine dynamics using diagonal recurrent neural network
8. Ren, Y. (2025). Torque Prediction of Internal Combustion Engines Based on Time Series. Proceedings of the 2025 2nd International Conference on Image Processing, Intelligent Control and Computer Engineering, Association for Computing Machinery.10.1145/3768184.3768249 ↗
9. Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine10.1016/j.apenergy.2004.07.008 ↗
10. Reinforcement learning for control: Performance, stability, and deep approximators10.1016/j.arcontrol.2018.09.005 ↗
11. Dai, H., Landry, B., Yang, L., Pavone, M., and Tedrake, R. (2021). Lyapunov-stable neural-network control. arXiv.10.15607/RSS.2021.XVII.063 ↗
12. ArjomandBigdeli, A., Mata, A., and Bak, S. (2024). Verification of neural network control systems in continuous time. International Symposium on AI Verification, Springer Nature.10.1007/978-3-031-65112-0_5 ↗
13. Robust multivariable control of an engine-dynamometer system10.1109/87.556024 ↗
14. High dynamic engine-dynamometer identification and control10.3182/20140824-6-ZA-1003.01141 ↗
15. Combustion engine test bed system identification under the presence of cyclic disturbances10.1109/TIE.2020.3016262 ↗
16. Prescribed performance robust approximate optimal tracking control via Stackelberg game10.1109/TASE.2025.3549114 ↗
17. Fixed-time stochastic learning from human-UAV interaction with state-input constraints10.1109/TIE.2025.3603074 ↗
18. Grobbelaar, E. (2017). The Development of a Small Diesel Engine Test Bench Employing an Electric Dynamometer. [Master’s Thesis, Stellenbosch University].
19. The MathWorks Inc. (2026, April 10). Calibrate, Validate, and Optimize SI Engine with Dynamometer Test Harness. Available online: https://ww2.mathworks.cn/help/autoblks/ug/explore-the-si-engine-dynamometer-reference-application.html.
20. Niemann, H., Miklos, R., and Poulsen, N.K. (2019). Grey box identification in closed-loop. American Control Conference (ACC), IEEE.10.23919/ACC.2019.8814460 ↗
21. Methods for closed loop system identification in industry
22. Shah, P., and Sekhar, R. (2019). Closed loop system identification of a DC motor using fractional order model. International Conference on Mechatronics, Robotics and Systems Engineering, IEEE.10.1109/MoRSE48060.2019.8998744 ↗
23. Dou, L., Zong, Q., Sun, L., and Ji, Y. (2009). Excitation signal design for closed-loop system identification. Chinese Control and Decision Conference, IEEE.
24. Shim, H. (2021). Disturbance observers. Encyclopedia of Systems and Control, Springer.10.1007/978-3-030-44184-5_100068 ↗
25. Simulation of a distance estimator for battery electric vehicle10.1016/j.aej.2015.04.008 ↗
26. NARMAX modelling and robust control of internal combustion engines10.1080/002071799221109 ↗
27. Zito, G., and Landau, I. (2005). NARMAX model identification of a variable geometry turbocharged diesel engine. American Control Conference, IEEE.10.1109/ACC.2005.1470094 ↗
28. Mottin, T., Oroski, E., Carlos, T.M., Silveira, L.B., and Alegre, J.A.A.V. (2023, January 15–18). Nonlinear system identification of a homogeneous charge compression ignition engine. Proceedings of the Simpósio Brasileiro de Automação Inteligente—SBAI, Manaus, Brazil.
29. Jamaludin, I.W., Wahab, N.A., Khalid, N.S., Sahlan, S., Ibrahim, Z., and Rahmat, M.F. (2013). N4SID and MOESP subspace identification methods. IEEE Colloquium on Signal Processing, IEEE.10.1109/CSPA.2013.6530030 ↗
30. The MathWorks Inc. (2026, April 10). What Are Nonlinear ARX Models? 2025. Available online: https://ww2.mathworks.cn/help/ident/ug/what-are-nonlinear-arx-models.html.
31. The MathWorks Inc. (2026, April 10). Hammerstein-Wiener Models. Available online: https://ww2.mathworks.cn/help/ident/hammerstein-wiener-models.html.
32. Counterbalancing speed control for hydrostatic drive heavy vehicle under long down-slope10.1109/TMECH.2014.2385700 ↗
33. Speed control with incremental algorithm of minimum fuel consumption tracking for variable speed diesel generator10.1016/j.enconman.2018.01.053 ↗
34. Adaptive cruise control look-ahead system for energy management of vehicles10.1016/j.eswa.2011.08.169 ↗
35. Huo, H., and Li, J. (1999). Cruise control based on gain scheduling PI diesel engine speed controller. IEEE International Vehicle Electronics Conference, IEEE.
36. Shams, T.A., Shah, S.I.A., Khushbash, S., and Ahmad, M.A. (2019). Evaluation of aerodynamic and stability performance parameters of high wing piston engine aircraft. 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, 8–12 January 2019, IEEE.10.1109/IBCAST.2019.8667193 ↗
37. Liu, Z., Gou, L., Fan, D., and Zhou, Z. (2019). Design of gain-scheduling robust controller for aircraft engine. Chinese Control Conference, IEEE.10.23919/ChiCC.2019.8866298 ↗
38. Aeroengine robust gain-scheduling control based on performance degradation10.1109/ACCESS.2020.2986336 ↗
39. The MathWorks Inc. (2026, April 10). Gain Scheduling. Available online: https://ww2.mathworks.cn/discovery/gain-scheduling.html.
40. Son, Y.I., Shim, H., Jo, N.H., and Kim, S.-J. (2007). Design of disturbance observer for non-minimum phase systems using PID controllers. SICE Annual Conference, IEEE.10.1109/SICE.2007.4420976 ↗
41. Disturbance observer for non-minimum phase linear systems10.1007/s12555-010-0508-x ↗