Fuzzy modelling techniques applied to an air/fuel ratio control system

A ratio control scheme decoupling disturbance response from set-point response

“…For instance, fuzzy logic techniques, dynamic sliding mode control and neural network algorithm have been applied to the airfuel ratio control [8][9][10][11][12][13][14][15][16]. Although these methods contribute to improved ratio control performance, they are based on numerical calculation, and thus, it is time-consuming and troublesome for implementation.…”

Air/fuel ratio control in SI1 engines using a combined neural network and estimator

“…Under the assumption of.plug flow in the exhaust manifold, the average velocity of the exhaust gases is -proportional to .the engine speed thus the transport delay vanes inversely with the engine speed but because of closely location of EGO sensor to exhaust valve and the blast of exhaust gases during the blowdown process dominates the this delay,z, varies with both dimensions of work point. Regarding that the sampling rate is based on crank angle, the equation modify this dynamics is[l]: where 9, :A/F ratio from gas that go into cylinder @e :A/F ratio from gas that go out from outlet manifold Gm :Measured signal from sensor and k ( k + 1) = YO$", ( k ) + Y d C ( k ) + Y2$r(k) (4) . mr …”

“…Some of solutions of this problem are based on providing some maps from engine bed test at entire work point space. For example, in hzzy control methods, these maps are applied extensively [4,5]. The solutions of this problem are based on estimation theory generally use identification techniques to find parameter values.…”