Feedback error learning neural networks for spark advance control using cylinder pressure

Abstract

This paper presents a spark advance control strategy based on the location of peak pressure (LPP) in spark ignition engines using artificial neural networks. The well-known problems of the LPP-based spark advance control method are that many samples of data are required and there is a problem detecting the combustion phasing owing to hook-back during lean burn operation. In order to solve these problems, a feedforward multilayer perceptron network (MLPN) is introduced in this study. The LPP and hook-back are estimated using the MLPN, which needs only five samples of output voltage from the cylinder pressure sensor. The estimated LPP can be regarded as an index for combustion phasing and can also be used as a minimum spark advance for best torque (MBT) control parameter. The performance of the spark advance controller is improved by adding a feedforward controller which reflects the abrupt changes of the engine operating conditions such as engine speed and manifold absolute pressure. The feedforward controller consists of the radial basis function network, and the feedback error learning method is used for the training of the network. In addition, the proposed control algorithm does not need sensor calibration and pegging (bias calculation) procedures because the MLPN estimates the LPP from the raw sensor output voltage. The feasibility of this methodology to control spark advances is closely examined through steady and transient engine operations. The experimental results have revealed that the LPP shows favourable agreement with the optimal value even during the transient operation of the engine.