Combustion engine modelling using an evolving local model network

In this paper a new evolving parameter estimation algorithm for a local model network under special consideration of combustion engine modelling is presented. For practical applications computational speed, incorporation of prior knowledge and the interpretability of the local models is of great interest. Accordingly, a robust and efficient online training algorithm with a particular focus on computational requirements involved in dynamic system identification of complex nonlinear processes is presented. The incremental construction of the model tree allows to gradually increase the model complexity while a proper initialisation of new model parameters is easily possible. The proposed evolving local model network is validated using real measurement data from a state-of-the-art 4-cylinder EUR05 diesel engine.