PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Robust predictive control of lambda in internal combustion engines using neural networks

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Stoichiometric air-to-fuel ratio (lambda) control plays a significant role on the performance of three way catalysts in the reduction of exhaust pollutants of Internal Combustion Engines (ICEs). The classic controllers, such as PI systems, could not result in robust control of lambda against exogenous disturbances and modeling uncertainties. Therefore, a Model Predictive Control (MPC) system is designed for robust control of lambda. As an accurate and control oriented model, a mean value model of a Spark Ignition (SI) engine is developed to generate simulation data of the engine's subsystems. Based on the simulation data, two neural networks models of the engine are generated. The identified Multi-Layer Perceptron (MLP) neural network model yields small verification error compared with that of the adaptive Radial Base Function (RBF) neural network model. Consequently, based on the MLP engine's model, the MPC system is performed through a nonlinear constrained optimization within gradient descent algorithm. The performance of the MPC system is compared with that of a first order Sliding Mode Control (SMC) system. According to simulation results, the tracking accuracy of lambda by the MPC system is close to that of the SMC system. However, the MPC system results in considerably smoother injected fuel signal.
Rocznik
Strony
432--443
Opis fizyczny
Bibliogr. 14 poz., rys., tab., wykr.
Twórcy
  • Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
autor
  • Department of Electrical Engineering, Amirkabir University of Technology, Hafez Ave., Hafez Street, PO Box 15875-4413, Tehran, Iran
autor
  • Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Bibliografia
  • [1] U. Kiencke, L. Nielsen, Automotive Control System for Engine, Driveline, and Vehicle, Springer, Berlin, 2004.
  • [2] Y.J. Zhai, D.L. Yu, Neural network model-based automotive engine air/fuel ratio control and robustness evaluation, Engineering Applications of Artificial Intelligence 22 (2009) 171-180.
  • [3] E. Hendricks, S.C. Sorenson, Mean value modeling of spark ignition engines. SAE Technical Paper Series, 900616, 1990.
  • [4] E. Hendricks, A. Chevalier, M. Jensen, S.C. Sorenson, Modeling of intake manifold filling dynamics. SAE Technical Paper Series, 960037, 1996
  • [5] S.W. Wang, D.L. Yu, Adaptive air-fuel ratio with MLP network, International Journal of Automation and Computing 2 (2005) 125-133.
  • [6] S.W. Wang, D.L. Yu, J.B. Gomm, G.F. Page, S.S. Douglas, Adaptive neural network model based predictive control for air fuel ratio of SI engines, Engineering Applications of Artificial Intelligence 19 (2006) 189-200.
  • [7] Y.J. Zhai, D.W Yu, H.Y. Guo, D.L. Yu, Robust air/fuel ratio control with adaptive DRNN model and AD tuning, Engineering Applications of Artificial Intelligence 23 (2010) 283-289.
  • [8] E. Hendricks, J. Luther, Model and observer based control of internal combustion engines, in: Proceedings of the 1st International Workshop on Modeling Emissions and Control in Automotive Engines, 2001, pp. 9-20.
  • [9] P. Kaidantzis, P. Rasmussen, M. Jensen, T. Vesterholm E. Hendricks, Robust, self-calibrating lambda feedback for SI engines. SAE Technical Paper 930860, 1993.
  • [10] C. Carnevale, D. Coin, M. Secco, P. Tubetti, A/F ratio control with sliding mode technique. SAE Technical Paper 950838, 1995.
  • [11] S.W. Wang, D.L. Yu, Adaptive neural network for parameter estimation and stable air fuel ratio control, Neural Networks 21 (2008) 102-112.
  • [12] M.B. Menhaj, Computational intelligence (Vol. 1): Fundamentals of Neural Networks, Tehran Polytechnic, Tehran, 2009.
  • [13] S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall International Inc., Upper Saddle River, New Jersey 07458, 1999.
  • [14] J. Keighobadi, S.M. Shahidi, K. Alioghli, Dynamic sliding mode controller for trajectory tracking of nonholonomic mobile robots, in: Proceedings of the 18th IFAC World Congress, Milano, Italy, August 28 September 2, 2011, pp. 962-967.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a1812317-d58b-461c-96f3-c7ef917c0797
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.