Closed-loop ignition control using online learning of locally-tuned radial basis function networks

Increasing demands of low emissions and low fuel consumption of modern spark ignition combustion engines require new ways for an optimal control of the ignition timing. Instead of classical open-loop strategies cylinder pressure sensors are used for an adaptive control of the ignition point. A linear feedback controller is designed as well as an online adaptive neural feedforward controller, the latter is trained during regular operation, i.e. no test cycles are required. The control algorithms were implemented and tested in a research automobile. Experimental results showed that the proposed neural network is very effective in learning the engine´s nonlinearities and in compensating for manufacturing tolerances and aging. The designed adaptive feedforward control improves efficiency and fuel consumption