Gaussian networks for fuel injection control

Abstract

This paper proposes a radial basis function (RBF) based approach for the fuel injection control problem. In the past, neural controllers for this problem have centred on using a cerebellar model articulation controller (CMAC) type network with some success. The current production engine control units also use look-up tables in their fuel injection controllers, and if adaptation is permitted to these look-up tables the overall effect closely mimics the CMAC network. Here it is shown that an RBF network with significantly fewer nodes than a CMAC network is capable of delivering superior control performance on a mean value engine model simulation. The proposed approach requires no a priori know..