In this paper, a new control algorithm for active suspension systems is presented. Latter is designed to improve low frequency comfort, which gains importance with autonomous driving and the possibility for nondriving tasks like in-car working. The algorithm is based on model predictive controllers and makes use of environment sensor information and extends preview by taking the future trajectory of the vehicle into account. A simulative benchmark demonstrates the advantages compared to existing control algorithms. Vertical- as well as roll- and pitch-acceleration can be significantly reduced by at least 20% compared to a non-preview controller up to a frequency of 5 Hz. A prototype vehicle implementation of the new controller enables a subjective and objective review of the resulting vehicle motion and confirms the results of the simulation. Despite of limitations on the precision of the preview information, vertical acceleration could be reduced by 5.5% in this implementation. It was shown that a future series implementation is possible and can be used to improve autonomous driving comfort by increasing low frequency suspension performance and efficient integration of additional features such as curve tilting.

Automated driving functions are expected to increase both the safety and ride comfort of future vehicles. Ensuring their functional safety and optimizing their performance requires thorough testing. Costs and duration of tests can be reduced if more tests can be performed numerically in a feasible simulation framework. This simulation setup must include all subsystems of the autonomous vehicle, which significantly interact with the system under test. In this paper, a modular model chain is presented, which is developed for testing systems with an impact on vehicle motion. It includes trajectory planning, motion control, and a model of the vehicle dynamics in a closed loop. Each subsystem can easily be exchanged to adapt the model chain with respect to the simulation objectives. As a use case, the testing of an active suspension control system is discussed. It is designed directly for use in autonomous cars and uses inputs from the vehicle motion planning subsystem for planning the suspension actuator motion. Using the presented closed-loop model chain, the effect of different actuator control strategies on ride comfort is compared, such as curve tilting. Furthermore, the impact of the active suspension system on lateral vehicle motion is shown.