Robust engine torque control by iterative learning control

Takashi Nagata, Masayoshi Tomizuka

Abstract: Fast-response engine torque control is robustly realized under repetitive air throttle input. An application of iterative learning control (ILC) to robustify the performance of disturbance observer (DOB) is proposed and numerically evaluated. The proposed scheme detects dynamical model discrepancy between an actual engine and its nominal model, and compensate for it to realize nominal plant dynamics. With the applied ILC realizing improved detection of model discrepancy, the scheme is significantly more effective than a conventional DOB under practical test-bench conditions such as measurement delays, noise, and insufficient data measurements.