Comparison of trajectory tracking controllers for emergency situations

Over the last years a number of different vehicle controllers has been proposed for tracking planned paths or trajectories. Most of previously published works do not compare their results with other approaches or limit the comparison to a few scenarios. Unfortunately, comparisons with existing controller concepts are very rare and a ranking is hard to establish from the literature. In this work, we rigorously compare inversion-based trajectory tracking controllers by systematically exploring the set of possible solutions when disturbances vary over time and initial states and parameters are uncertain. By using Monte-Carlo simulation, we determine the average performance and by using rapidly exploring random trees, we determine the worst-case performance, which is especially important in emergency situations when avoiding a crash is essential. The tested scenarios and the applied methodologies are documented in detail so that they serve as benchmark problems for other control concepts. The results show that the controller with smaller relative degree performs better with respect to the worst-case deviation computed by rapidly exploring random trees, while conventional simulations of random scenarios would not reveal any difference.