Inversion of fuzzy neural networks for the reduction of noise in the control loop for automotive applications

• Autorzy: Nentwig M. , Mercorelli P.
• Języki publikacji: EN

Abstrakty

EN

A robust throttle valve control has been an attractive problem since throttle by wire systems were established in the mid-nineties. Control strategies often use a feed-forward controller which use an inverse model; however, mathematical model inversions imply a high order of differentiation of the state variables resulting in noise effects. In general, neural networks are a very effective and popular tool for modelling. The inversion of a neural network makes it possible to use these networks in control problem schemes. This paper presents a control strategy based upon an inversion of a feed-forward trained local linear model tree. The local linear model tree is realized through a fuzzy neural network. Simulated results from real data measurements are presented, and two control loops are explicitly compared.

Słowa kluczowe

EN

neural networks; fuzzy control; inversion of neural networks; automotive control; noise reduction

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2009
• Tom: Vol. 3, No. 3
• Strony: 83--89
• Opis fizyczny: Bibliogr. 13 poz.,rys.

Twórcy

autor

Nentwig M.

autor

Mercorelli P.

• University of Applied Sciences, Braunschweig/Wolfenbuettel, Wolfsburg, Germany,

Bibliografia

• [1] Nakano K., , “Modelling and observer-based sliding-mode control of electronic throttle systems”, ECTI Trans. Electrical Eng., Electronics and Communications, vol. 4, no. 1, 2006, pp. 22-28.
• [2] Fink A., Nelles O., “Nonlinear internal model control based on local linear neural networks”. In:IEEE Systems, Man, and Cybernetics, Tucson, USA, 2001.
• [3] Fink A., Nelles O., Fischer M., “Linearization based and local model based controller design”. In:European Control Conference (ECC), Karlsruhe, Germany, 1999.
• [4] Fink A., Toepfer A.,On the inversion of nonlinear models.Technical report, University of Darmstadt, 2003.
• [5] Fink A., Toepfer S., Isermann R., “Neuro and neurofuzzy identification for model-based control”. In:IFAC Workshop on Advanced Fuzzy/Neural Control, Valencia, Spain, vol. Q, 2001, pp. 111-116.
• [6] Fink A., Toepfer S., Isermann R., “Nonlinear modelbased control with local linear neuro-fuzzy models”,Archive of Applied Mechanics, vol. 72, no. 11-12, 2003, pp. 911-922.
• [7] Fischer M., Nelles O., Fink A., “Supervision of non-linear adaptive controllers based on fuzzy models“. In:14 IFAC World Congress, Beijing, China, vol. Q, 1999, pp. 335-340.
• [8] Mercorelli P., “An optimal minimum phase approximating pd regulator for robust control of a throttle plate“. In: 45 IEEE Conference on Decision and Control (CDC2006), San Diego (USA), 13 -15 December, 2006.
• [9] Nelles O.,Nonlinear System Identification with Local Linear Neuro-Fuzzy Models. Shaker Verlag, 1999.
• [10] Nelles O., Nonlinear System Identification, Springer Verlag, 2001.
• [11] Nelles O., Fink A., Isermann R. “Local linear model trees (lolimot) toolbox for nonlinear system identification“. In: 12 IFAC Symposium on System Identification(SYSID), Santa Barbara, USA, 2000.
• [12] Nentwig M., Mercorelli P., “A matlab/simulink tool-box for inversion of local linear - model trees“.in press.
• [13] Rossi C., Tilli A., Tonielli A., “Robust control of a throttle body for drive by wire operation of automotive engines“. In: IEEE Trans. Contr. Syst. Technology, vol. 8, no. 6, 2000, pp. 993-1002.