Data-driven control approaches have been proved highly effective in applications where the dynamics of the system are unknown. The reason is that the use of data in control overcomes the challenge of parametric modeling which requires effort and often leads to an insufficient description of the system’s behavior. By contrast, the behavior of the system can be captured more accurately when input-output data is used. In this project a frequency-domain approach is proposed for robust controller design for controllers of pre-specified structure. Specifically, an existing technique is extended so that it guarantees not only nominal, but also robust performance. This is achieved by ensuring performance for multiple frequency-domain models acquired from data. The related computational efficiency in the overall approach is also a fundamental criterion and thus we propose a series of techniques by which it can be improved. Additionally, a major contribution of this work is an iterative algorithm that minimizes a linear approximation of the H infinity norm of the closed-loop system. Last but not least, a method to optimize the linear constraints of the optimization scheme is proposed.

Commercial heavy vehicles are especially prone to rolling over due to their inherent properties, such as the high centre of gravity - track width ratio and compliant chassis frame. Autonomous trucks cannot become widespread without eliminating this danger by guaranteeing rollover-free vehicle motion. Currently existing Roll Stability Control implementations rely on the assumption of having a responsible driver behind the steering wheel -- therefore, unsupervised driving will need a higher level of roll safety than what the currently used methods can provide. Fortunately, there are two main attributes of self-driving vehicles that can be utilized to achieve this goal: Information about the reference path ahead of the truck will be available and the used algorithms may have a full control authority over the available actuators.

This thesis project developed two, redundant rollover mitigation techniques to be run in parallel, for a tractor-trailer combination: The proactive and reactive Roll Stability Control methods. These vehicle motion controllers are separate, independent functional entities.

While the proactive approach attempts to prevent upcoming events, the reactive Roll Stability Control is designed to mitigate imminent rollovers that could not be anticipated based on motion reference information. This controller is placed within the motion control paradigm of Control Allocation. The objective of Control Allocation is to coordinate different actuators to achieve both longitudinal and yaw accelerations as desired by the higher-level tracking controller. Roll stability is achieved by extending the set of functionalities of this framework, using both brakes and steering to realize the needed interventions. Emphasis is put on accurate wheel lift-off detection, using lateral acceleration, steering angle and accurately estimated roll angle signals.

Design choices during syntheses of both controllers were made based on the conclusions of a thorough analysis of roll dynamics, carried out using a high-fidelity vehicle model, provided and validated by Volvo Group Truck Technologies. Subsequently, both controllers were implemented within Volvo's real-time framework. While the proactive method's performance was only assessed using simulations, the reactive controller was tested on Volvo's proving grounds.

The thesis concludes that achieving a higher level of roll stability of (autonomous) heavy vehicles is possible, whilst having a less conservative overall behaviour compared to traditional approaches. This research contributes to making a step towards the next generation of rollover prevention for automated trucks.