Associative datafields in automotive control

This work presents an associative datafield structure that has been developed for automotive control applications by the Institute of Control Engineering, Department of Control Systems Theory and Robotics at the Technical University of Darmstadt and the Robert Bosch GmbH. In contrast to the state-of-the-art lattice-like datafields, the new system permits the modelling of multidimensional nonlinear process and/or controller characteristics with respect to computational performance and storage capacity provided by automotive control units. Furthermore, the associative datafield allows the compensation of wear and tear and manufacturing tolerances by learning, i.e. online adaptation of its contents. The paper describes the principles of the associative datafield and different update algorithms. Results of simulations using realistic data from car engines are discussed