In the contribution, a model predictive trajectory tracking approach is presented. Due to the utilization of an accurate prediction model, which considers not only the vehicle dynamics but also the limited actuator dynamics, the approach can be used even in emergency collision avoidance systems. The approach explicitly predicts a trajectory set for defined control inputs. Out of the set, the trajectory which is closest to the reference is selected. Two different objective functions are defined, each of them selecting the optimum input variable for trajectory tracking. On the one hand, the selection is based on the predicted position trajectories and, on the other hand, on the speed and yaw rate of the trajectory set. The evaluation is carried out in the simulation with a vehicle model for which different error sources, like sensor errors, sensor noise, and static friction are modeled using data from a real vehicle. This development method allows a direct and fast transferability into the real test vehicle.

This paper presents two approaches for the determination of a feedforward for lateral trajectory tracking control. Most trajectory planning approaches for automated vehicles are based on a mass point description. Thus, the reference trajectory only provides information about the position in x- and y-coordinates over time. Therefore, this paper proposes a method to estimate the side-slip angle and to calculate the yaw rate from the course rate. With this additional enriching information about the reference trajectory, a feedforward control based on the inversion of the linear single-track model is introduced. On the other hand, a flat output is used to invert the nonlinear single-track model and thus determine a feedforward control.

The contribution at hand combines a sampling-based trajectory planning approach and a model predictive trajectory tracking controller to a collision avoidance system. The planner generates candidate trajectories by the suitable selection of breakpoints which are connected by a spline interpolation. A procedure is presented to systematically select sample states to perform a collision avoidance maneuver in case of an emergency situation. The vehicle is controlled to the optimal trajectory of the planner by comparison with a model predictive trajectory set. This is determined by the prediction of a detailed nonlinear vehicle model for constant input variables. A suitable objective function with a time weighting is utilized to evaluate the individual trajectories and to select the optimal input variables.

A systematic approach to identify the steering behavior and to design a nonlinear controller by utilizing experimental data is presented in this contribution. The used measurements of steering maneuvers are recorded in a test vehicle equiped with an electrically powered belt drive steering system. In a first step, the system order is approximated by determining the amplitude and frequency characteristics in the frequency domain through correlation methods. By using step responses a parametric identification of the steering behavior is done in the time domain. The determined model is the basis for the design of a nonlinear controller. Since various parameters which influence the steering behavior are dependent on external environmental factors and highly time invariant, a robust PID controller with gain scheduling and a feed forward is utilized.

The presented approach combines the planning of trajectories and the vehicle control during emergency maneuvers. For this purpose an approach is utilized, which predicts the future behavior of the actuators and the vehicle with a nonlinear model. The input space is roughly discretized and a trajectory set is calculated explicitly. The choice of optimal inputs is performed by a direct comparison of the possible trajectories, in contrast to model predictive control. The discretization is carried out adaptively depending on the current reference input. Issues arising from the limited degree of freedom are solved by an additional transition time within the prediction horizon. Model inaccuracies are taken into account during the objective function evaluation, by utilizing a soft constraint function, which increase the distance to objects and street boundaries.