An adaptive iterative learning control scheme for reducing CO2 emission in gasoline engines

This paper proposes a control algorithm for a Toyota gasoline engine problem that is addressed in a student competition format. The control objective is to minimize the fuel consumption while avoiding specified dangerous situations. The approach develops a feed-forward control based on an adaptive Iterative Learning Control. In this method, the plant is run several times and the controller iteratively updates the actuation inputs in order to generate the desired reference torque profile. The algorithm converges after approximately 10 iterations providing the corresponding locally optimal control trajectories.