Nonlinear distance and cruise control for passenger cars

A methodology for designing an automated vehicle longitudinal velocity and distance controller is presented and applied to an automobile. nonlinearities. The controller consists of a three layer structure. In the first layer a linearization of the nonlinearities is done in order to achieve a simplified structure for controller design purposes. With respect to changes in the typical vehicle parameters-mass and aerodynamical drag-an adaptive controller structure is used and these parameters are estimated by a recursive least squares algorithm. Based on classical controlling techniques a linear acceleration controller is developed in the middle layer. A fuzzy controller is applied in the upper layer. This controller based on the linguistic description of comfort demands. Additionally a neural network is used in this layer instead of the fuzzy system. The complete structure is used in two different series fabricated vehicles and experimental results are shown for highway traffic and also for stop-go traffic on highway congestions. Additionally a neural network is trained by measurement data as a second approach to describe the comfort demands