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Electronic throttle control is extensively preferred to vary the air intake in the engine manifold for regulating the torque in order to obtain the better vehicle response, high performance in terms of improving the fuel economy and trim down the emissions of the spark ignition engines. For such type of the engine control systems the throttle angle is estimation is accomplished either by pedal follower or torque based method. This work aims to develop a throttle opening angle estimation strategy in a closed loop manner using fuzzy logic approach by considering real time internal system and driver torque demands for controlling the SI engine. In present work the torque demand from internal system such as catalyst heating, cold start assist and battery voltage compensation is estimated using fuzzy logic strategy. Such intelligent system aims to replace the lookup tables associated with those systems and reduces the calibration effort.

Electronic throttle body (ETB) is commonly employed in an intake manifold of a spark ignition engine to vary the airflow quantity by adjusting the throttle valve in it. The actual position of the throttle valve is measured by means of a dual throttle position sensor (TPS) and the signal is feedback into the control unit for accomplishing the closed loop control in order handle the nonlinearities due to friction, limp-home position, aging, parameter variations. This work aims presents a neural networks based novel virtual sensor for the estimation of throttle valve position in the electronic throttle body. Proposed neural network model estimates the actual throttle position using three inputs such as reference throttle angle, angular error and the motor current. In the present work, the dynamic model of the electronic throttle body is used to calculate the current consumed by the motor for corresponding throttle valve movement.