On-line control for optimal ignition timing using the pseudolinear radial basis function and the local linear model tree

Abstract

A new generation of engines demands new control strategies. The increased number of control variables of variable valve timing engines results in complexity of conventional control structures. This necessitates the integration of new technologies for optimal control of the ignition timing. This paper presents a neural network controller for ignition timing that uses two recently proposed new neural network structures—a pseudolinear radial basis function (PLRBF) network and a local linear model tree (LOLIMOT) network. Tests showed that the relative load signal is not necessary to evaluate the ignition angle, and therefore no air mass meter is necessary. The two neural networks are compared with a conventional look-up table control structure. The network controller improves the conventional look-up table method for calibration by comparison with bilateral look-up tables. The neural controller is implemented and tested in a research car. Experimental results show that the neural networks are very effective in mapping non-linearity. The design of the neural network controller simplifies the structure drastically.