Comfort oriented nonlinear model predictive control

Abstract

To promote automation in vehicles, autonomous driving should feel comfortable. To achieve low discomfort, a comfort oriented nonlinear model predictive controller is created. We know humans are sensitive for discomfort in certain frequencies in acceleration. By penalizing the frequencies for discomfort a higher comfort performance can be achieved. Two band pass filters are created to penalise the frequencies. A band pass filter (0.03-0.2 Hz) for the frequency of motion sickness and a band pass filter (1-2 Hz) for general discomfort. Due to the MPC framework the filters can be implemented on the predicted accelerations. The filtered accelerations are penalised within the MPC. The MPC is made for path following control. To test the MPC a reference generator is built. The reference generator creates reference signals about the path ahead for the controller. To test the performance of the filters, tests are done with different controllers. In the different controllers the filters are penalised individually and together and compared against other controllers. The controllers are tested on multiple scenarios (e.g. double lane change and a sinusoidal trajectory). On the scenarios multiple disturbances are tested (e.g. wind disturbance and sensor noise). We conclude that the MPC design that relies on the motion sickness filter has a significant decrease of motion sickness in scenarios where a lot of motion sickness is present, with improvements up to 30.7% compared to the basic controller. The MPC design that relies on the general discomfort filter helps bring the general discomfort down. On the double lane change maneuver with a changing velocity the general discomfort filter has improvements up to 10.7% compared to the basic controller.