Control of 12-Cylinder Camless Engine with Neural Networks

Department of Mechanical and Industrial Engineering, American University of Ras Al Khaimah, UAE
Department of Mechanical Engineering, Hashemite University, Zarqa, Jordan

Abstract

The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s). The inputs to the net are the intake valve lift (IVL) and intake valve closing timing (IVC) whereas the output of the net is the cylinder air charge (CAC). The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and applied to the camless engine ANN model. As a consequence the overall 12-cyliner camless engine feedback controller is upgraded and the necessary changes are implemented in order to contain the adaptive neural network with the objective of tracking the cylinder air charge (driver’s torque demand) while minimizing the pumping losses (increasing engine efficiency). All the needed measurements are extracted only from the two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF) and the intake manifold absolute pressure (MAP) sensors. The feedback controller’s capability is demonstrated through computer simulation.