Iterative learning identification for an automated off-highway vehicle

This paper presents a new approach for identifying the lateral dynamics of an automated off-highway agricultural vehicle. A second order model is proposed to represent the vehicle lateral dynamics. An Iterative Learning Identification (ILI) method is used to identify the model parameters. Simulation and experimental results show the convergence of parameters with arbitrarily chosen initial estimations. The estimation results are compared to other traditional identification methods: least square estimation and gradient based adaptive estimation. The results highlight the practical benefit of the ILI approach-i.e. that it can be performed in a relatively small section of field and therefore done prior to actual usage or engagement with crops.