Abstract
This paper presents modelling of internal combustion (IC) engine with adaptive neural networks. A radial basis function network model with both centres and weights adapted and a model with only weights adapted are compared with a fixed parameter model. The developed models are used in model based predictive control (MPC) to form an adaptive nonlinear MPC scheme and applied to engine speed tracking control. The modelling and control are based on a generic mean value engine model and consists of three submodels that describe the fuel mass flow dynamics, the intake manifold filling dynamics and the crankshaft speed. Adaptive MPC is shown superior over the fixed parameter model based control.
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