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.

System identification for switched linear systems from input output data has received substantial attention in recent years. There is a growing interest for techniques that pose the identification problem as a sparse optimisation problem. At the same time a vast amount of research is dedicated to improving SAT solvers which as a result become faster every year. In this work a novel identification method for Switched AutoRegressive eXogenous (SARX) systems
and PieceWise AutoRegressive eXogenous (PWARX) systems is proposed that combines sparse optimisation with a SAT solver. The presented method aims to minimise the number of submodels needed to fit the data, while facilitating a prescribed minimum dwell time between switches. The procedure for the identification of switched ARX models is composed of two steps. The First phase determines the switching times in an iterative process aided by a SAT solver. Second, the model parameters and the switching sequence are estimated by
optimising the sparsity of a sequence. The identification procedure for PWARX models operates similarly although it incorporates the knowledge that switches depend on the regressor. An extension to these methods that makes the identification of large datasets tractable is also put forward. The proposed algorithm is evaluated on synthetic systems from the literature and shows promising results. Finally, the proposed algorithm is applied to an experimental benchmark dataset for a nonlinear system. All the algorithms proposed in this thesis project are implemented in the form of a toolbox that is made publicly available.