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Automatic control of automotive engines provides benefits in the engines performancelike emission reduction and fuel economy. The drop in idle speed problem can be seen as thedisturbance rejection problem in the main engine speed. In this paper, a PID-like Fuzzy LogicControl (PIDFC) with minimum structure for the four strokes, four cylinders, gasoline engine isdesigned and simulated to maintain the engine speed at nominal value in idle speed mode. Thespeed performance must satisfy minimize fuel consumption, and as a result reduces the fuelemissions. A spiking Neural Network (SNN) trained by Particle Swarm Optimization (PSO)algorithm is proposed to online-adapt the inputs and output gains of the PID fuzzy controller inorder to achieve the required speed performance. A Mean Value Engine Model (MVEM) is used tosimulate nonlinear model of engine. .Results of simulation for this controller showed goodimprovements over the PIDFC in the idle speed response. .The peak overshoot is reduced about(70 %), the undershoot is reduced about (50 %), the settling time is. .reduced about (83%) and thefuel consumed is reduced about (53%).

mean value engine model --- fuzzy logic control and spiking neural network