Integrated methodology for state and parameter estimation of spark-ignition engines

References

• Betts, J. T. (2010). Practical methods for optimal control and estimation using nonlinear programming. Society for Industrial and Applied Mathematics.
   Google Scholar
• Butt, Q. R., & Bhatti, A. I. (2008). Estimation of gasoline-engine parameters using higher order sliding mode. IEEE Transactions on Industrial Electronics, 55(11), 3891–3898. https://doi.org/10.1109/TIE.2008.2005930
   Web of Science ®Google Scholar
• Cassidy, J., Athans, M., & Lee, W. H. (1980). On the design of electronic automotive engine controls using linear quadratic control theory. IEEE Transactions on Automatic Control, 25(5), 901–912. https://doi.org/10.1109/TAC.1980.1102457
   Web of Science ®Google Scholar
• Cavina, N., & Suglia, R (2005). Spark advance control based on a grey box model of the combustion process (SAE Technical Paper 2005-01-3760).
   Google Scholar
• Chandrasekar, J., Kim, I., Bernstein, D. S., & Ridley, A. J. (2008). Reduced-rank unscented Kalman filtering using Cholesky-based decomposition. International Journal of Control, 81(11), 1779–1792. https://doi.org/10.1080/00207170801891724
   Web of Science ®Google Scholar
• Chen, A. S., Na, J., Herrmann, G., Burke, R., & Brace, C. (2017). Adaptive air-fuel ratio control for spark ignition engines with time-varying parameter estimation. In 9th International Conference on Modelling, Identification and Control (ICMIC) (pp. 1074–1079). IEEE.
   Google Scholar
• Chin, Y. K., & Coats, F. E. (1986). Engine dynamics: Time-based versus crank-angle based. SAE Transactions, 95, 936–956. https://www.jstor.org/stable/44722685
   Google Scholar
• Cook, J. A., & Powell, B. K. (1988). Modeling of an internal combustion engine for control analysis. IEEE Control Systems Magazine, 8(4), 20–26. https://doi.org/10.1109/37.7726
   Google Scholar
• Cui, T., Ding, F., Alsaedi, A., & Hayat, T. (2020). Data filtering-based parameter and state estimation algorithms for state-space systems disturbed by coloured noises. International Journal of Systems Science, 51(9), 1669–1684. https://doi.org/10.1080/00207721.2020.1772403
   Web of Science ®Google Scholar
• Curtis, E. W., Aquino, C. F., Trumpy, D. K., & Davis, G. C. (1996). A new port and cylinder wall wetting model to predict transient air/fuel excursions in a port fuel injected engine. SAE Transactions 105, Section 3: Journal of Engines, 1561–1572. https://www.jstor.org/stable/44736382
   Google Scholar
• De Nicolao, G., Scattolini, R., & Siviero, C. (1996). Modelling the volumetric efficiency of IC engines: parametric, non-parametric and neural techniques. Control Engineering Practice, 4(10), 1405–1415. https://doi.org/10.1016/0967-0661(96)00150-5
   Web of Science ®Google Scholar
• Ding, F. (2014). Combined state and least squares parameter estimation algorithms for dynamic systems. Applied Mathematical Modelling, 38(1), 403–412. https://doi.org/10.1016/j.apm.2013.06.007
   Web of Science ®Google Scholar
• Ebrahimi, B., Tafreshi, R., Masudi, H., Franchek, M., Mohammadpour, J., & Grigoriadis, K. (2012). A parameter-varying filtered PID strategy for air–fuel ratio control of spark ignition engines. Control Engineering Practice, 20(8), 805–815. https://doi.org/10.1016/j.conengprac.2012.04.001
   Web of Science ®Google Scholar
• Evensen, G. (2009). Data assimilation: The ensemble Kalman filter. Springer.
   Google Scholar
• Fang, H., Tian, N., Wang, Y., Zhou, M., & Haile, M. A. (2018). Nonlinear Bayesian estimation: From Kalman filtering to a broader horizon. IEEE/CAA Journal of Automatica Sinica, 5(2), 401–417. https://doi.org/10.1109/JAS.2017.7510808
   Google Scholar
• Franchek, M. A., Buehler, P. J., & Makki, I. (2007). Intake air path diagnostics for internal combustion engines. Journal of Dynamic Systems, Measurement, and Control, 129(1), 32–40. https://doi.org/10.1115/1.2397150
   Web of Science ®Google Scholar
• Gao, J., Zhang, Y., & Shen, T. (2017). An on-board calibration scheme for map-based combustion phase control of spark-ignition engines. IEEE/ASME Transactions on Mechatronics, 22(4), 1485–1496. https://doi.org/10.1109/TMECH.2017.2696788
   Web of Science ®Google Scholar
• Guillemin, F., Grondin, O., Chauvin, J., & Nguyen, E (2008). Combustion parameters estimation based on knock sensor for control purpose using dedicated signal processing platform (SAE Technical Paper 2008-01-0790).
   Google Scholar
• Guzzella, L., & Onder, C. (2010). Introduction to modeling and control of internal combustion engine systems (2nd ed.). Springer.
   Google Scholar
• Hendricks, E. (2000). A generic mean value engine model for spark ignition engines. In Proceedings of the 41st Simulation Conference. SIMS.
   Google Scholar
• Hendricks, E., Chevalier, A., Jensen, M., Sorenson, S. C., Trumpy, D., & Asik, J. (1996). Modelling of the intake manifold filling dynamics. SAE Transactions, 105, Section 3: Journal of Engines, 122–146.
   Google Scholar
• Hendricks, E., & Sorenson, S. C. (1990). Mean value modelling of spark ignition engines. SAE Transactions, 99, Section 3: Journal of Engines, Part 2, 1359–1373. https://www.jstor.org/stable/44736261
   Google Scholar
• Isermann, R., & Münchhof, M. (2010). Identification of dynamic systems: An introduction with applications. Springer.
   Google Scholar
• Johnstone, R., Johnson Jr, C. R., Bitmead, R. R., & Anderson, B. (1982). Exponential convergence of recursive least squares with exponential forgetting factor. Systems & Control Letters, 2(2), 77–82. https://doi.org/10.1016/S0167-6911(82)80014-5
   Web of Science ®Google Scholar
• Julier, S (2002). The scaled unscented transformation. In American Control Conference (pp. 4555–4559). IEEE.
   Google Scholar
• Julier, S., Uhlmann, J., & Durrant-Whyte, H (1995). A new approach for filtering nonlinear systems. In American Control Conference (pp. 1628–1632). IEEE.
   Google Scholar
• Julier, S., Uhlmann, J., & Durrant-Whyte, H. F. (2000). A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 45(3), 477–482. https://doi.org/10.1109/9.847726
   Web of Science ®Google Scholar
• Kandepu, R., Foss, B., & Imsland, L. (2008). Applying the unscented Kalman filter for nonlinear state estimation. Journal of Process Control, 18(7–8), 753–768. https://doi.org/10.1016/j.jprocont.2007.11.004
   Web of Science ®Google Scholar
• Kim, Y. W., Rizzoni, G., & Utkin, V. (1998). Automotive engine diagnosis and control via nonlinear estimation. IEEE Control Systems Magazine, 18(5), 84–99. https://doi.org/10.1109/37.722255
   Web of Science ®Google Scholar
• Kulikov, G. Y., & Kulikova, M. V. (2017). Accurate continuous–discrete unscented Kalman filtering for estimation of nonlinear continuous-time stochastic models in radar tracking. Signal Processing, 139, 25–35. https://doi.org/10.1016/j.sigpro.2017.04.002
   Web of Science ®Google Scholar
• Lin, F. J. (1996). Application of EKF and RLS estimators in induction motor drive. In 27th Annual IEEE Power Electronics Specialists Conference (pp. 713–718). IEEE.
   Google Scholar
• Manzie, C., Palaniswami, M., Ralph, D., Watson, H., & Yi, X. (2002). Model predictive control of a fuel injection system with a radial basis function network observer. Journal of Dynamic Systems, Measurement, and Control, 124(4), 648–658. https://doi.org/10.1115/1.1515328
   Web of Science ®Google Scholar
• Maree, J. P., Imsland, L., & Jouffroy, J. (2016). On convergence of the unscented Kalman–Bucy filter using contraction theory. International Journal of Systems Science, 47(8), 1816–1827. https://doi.org/10.1080/00207721.2014.953799
   Web of Science ®Google Scholar
• Menegaz, H. M., J. Y. Ishihara, Borges, G. A., & Vargas, A. N. (2015). A systematization of the Unscented Kalman filter theory. IEEE Transactions on Automatic Control, 60(10), 2583–2598. https://doi.org/10.1109/TAC.2015.2404511
   Web of Science ®Google Scholar
• Powell, J. D., Fekete, N., & Chang, C. F. (1998). Observer-based air fuel ratio control. IEEE Control Systems Magazine, 18(5), 72–83. https://doi.org/10.1109/37.722254
   Web of Science ®Google Scholar
• Shi, Z., & O'Brien, W. (2019). Sequential state prediction and parameter estimation with constrained dual extended Kalman filter for building zone thermal responses. Energy and Buildings, 183, 538–546. https://doi.org/10.1016/j.enbuild.2018.11.024
   Web of Science ®Google Scholar
• Singh, V., Pal, B., & Jain, T (2020). State and parameter estimation for spark ignition engine with parameter uncertainty. In 28th Mediterranean Conference on Control and Automation (MED'2020) (pp. 502–507). IEEE.
   Google Scholar
• Sun, Y., Li, L., Yan, B., Yang, C., & Tang, G. (2016). A hybrid algorithm combining EKF and RLS in synchronous estimation of road grade and vehicle mass for a hybrid electric bus. Mechanical Systems and Signal Processing, 68, 416–430. https://doi.org/10.1016/j.ymssp.2015.08.015
   Web of Science ®Google Scholar
• Tang, H., Weng, L., Dong, Z. Y., & Yan, R. (2009). Adaptive and learning control for SI engine model with uncertainties. IEEE/ASME Transactions on Mechatronics, 14(1), 93–104. https://doi.org/10.1109/TMECH.2008.2004806
   Web of Science ®Google Scholar
• Teixeira, B. O., Chandrasekar, J., Tôrres, L. A., Aguirre, L. A., & Bernstein, D. S. (2009). State estimation for linear and non-linear equality-constrained systems. International Journal of Control, 82(5), 918–936. https://doi.org/10.1080/00207170802370033
   Web of Science ®Google Scholar
• Vargas, A. N., Menegaz, H. M., Ishihara, J. Y., & Acho, L. (2016). Unscented Kalman filters for estimating the position of an automotive electronic throttle valve. IEEE Transactions on Vehicular Technology, 65(6), 4627–4632. https://doi.org/10.1109/TVT.2016.2518018
   Web of Science ®Google Scholar
• Wan, E. A., & Nelson, A. T (2001). Dual extended kalman filter methods. In S. Haykin (Ed.), Kalman filtering and neural networks (pp. 123–173). Wiley.
   Google Scholar
• Wan, E. A., & Van Der Merwe, R (2000). The unscented Kalman filter for nonlinear estimation. In Adaptive systems for signal processing, communications, and control symposium (pp. 153–158). IEEE.
   Google Scholar
• Wang, S., Yu, D., Gomm, J., Page, G., & Douglas, S. (2006). Adaptive neural network model based predictive control for air–fuel ratio of SI engines. Engineering Applications of Artificial Intelligence, 19(2), 189–200. https://doi.org/10.1016/j.engappai.2005.08.005
   Web of Science ®Google Scholar
• Yazdani, A., Naber, J., Shahbakhti, M., Dice, P., Glugla, C., & Cooper, S. McEwan (2017). Air charge and residual gas fraction estimation for a spark-ignition engine using in-cylinder pressure (SAE Technical Paper 2017-01-0527 13).
   Google Scholar
• Yildiz, Y., Annaswamy, A., Yanakiev, D., & Kolmanovsky, I. (2010). Spark ignition engine fuel-to-air ratio control: An adaptive control approach. Control Engineering Practice, 18(12), 1369–1378. https://doi.org/10.1016/j.conengprac.2010.06.011
   Web of Science ®Google Scholar