This paper newly proposes an on-line learning method for control maps by using Gaussian filters. In the present method, man-hours for calibration of control maps can be decreased, and complicated structures of control maps are learned without prior knowledge. Moreover, by the effect of Gaussian filtering, smoothed maps can be created even under noisy conditions or few measured points. We also introduce improvements of the algorithm to cope with engine deterioration due to aging. In this work, the proposed method is applied to minimum advance for best torque control on actual vehicles with just one driving data, and the accuracy of the learned map is verified through simulation and experiments.
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