Title :
Air to fuel ratio control of spark ignition engines using dynamic sliding mode control and Gaussian neural network
Author :
Won, Mooncheol ; Choi, Sei-Bum ; Hedrick, J.K.
Author_Institution :
Dept. of Mech. Eng., California Univ., Berkeley, CA, USA
Abstract :
This paper deals with air to fuel ratio control of a spark ignition engine, whose pollutant is a major cause of air pollution. A direct adaptive control using Gaussian neural networks is developed to compensate transient fueling dynamics and measurement error in mass air flow rate into the cylinder. The transient fueling compensation method is coupled with a dynamic sliding mode control technique that governs the steady state fueling rate. The proposed controller is simple enough for online computation and is implemented automotive engine using a PC-386
Keywords :
adaptive control; automobiles; flow control; fuel optimal control; internal combustion engines; mechanical engineering computing; neural nets; neurocontrollers; variable structure systems; Gaussian neural network; air to fuel ratio control; direct adaptive control; dynamic sliding mode control; mass air flow rate; spark ignition engines; transient fueling compensation; Adaptive control; Air pollution; Engines; Fuels; Ignition; Measurement errors; Neural networks; Sliding mode control; Sparks; Vehicle dynamics;
Conference_Titel :
American Control Conference, Proceedings of the 1995
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2445-5
DOI :
10.1109/ACC.1995.532316
