Identification and control of an automated off highway agricultural vehicle

Abstract

The main objective of this study was to develop an automated agricultural vehicle guidance system that can be easily transplanted from vehicle to vehicle. The proposed solution to this problem is to first perform a tractor model identification, and then use a pole placement technique to place the closed loop dominant poles in their desired locations. One of the most difficult aspects of designing a controller for vehicle guidance is arriving at a good model of vehicle lateral dynamics. This study presents a new approach for identifying the lateral dynamics of an automated off-highway 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 squares estimation and gradient based adaptive estimation. The results highlight the practical benefits 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.