Traction control using artificial neural networks

TL;DRAbstract

Modern traction control techniques manage driven wheel speed from data obtained through a small number of sensors from around an automobile. This data is then fed into an on-board computer that has been specially programmed to attempt to maximise driven wheel traction from its information base. Problems can arise, however, when abnormal conditions are encountered. The small number of sensors used as controller inputs and the various assumptions made during the conventional controller programming can lead to erroneous correction commands ‚ÄövÑvÆ with an increased risk of spinout and other undesirable situations. The incorporation of artificial neural networks into the traction controller command logic has the scope to bring a two-fold advantage. The first stems from a neural network's ability to learn. It can be programmed through experimental data, and thus removes the need for tedious mathematics programming of the vehicle dynamics, which can be used to reduce controller cost and also

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