Gaussian networks for fuel injection control

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 eVect closely mimics the CMAC network. Here it is shown that an RBF network with signi cantly 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 knowledge of the engine systems, and on-line learning is achieved using gradient descent updates. The RBF network is then implemented on a four-cylinder engine and, after a minor modi cation, outperforms a production engine control unit.