Over the past few decades vehicles have become more safe than ever. Not only has the chassis design improved substantially, the addition of things such as airbags and seat belts has significantly reduced the fatality rate of passengers involved in traffic accidents. A more recent development are driver assistance systems such as the anti-lock braking system (ABS) and the electronic stability program (ESP). ABS prevents wheel lock during emergency braking and ESP preserves lateral stability. These are active systems designed to help the driver maintain control of the vehicle and prevent dangerous situations from occurring.

One type of accident that has not been addressed enough by these driver assistance systems is the rollover accident. Though a relatively infrequent occurrence, rollover accidents are extremely dangerous. According to statistics from the NHTSA the passenger fatality rate for rollover accidents is almost fourteen times as high compared to the overall accident fatality rate. In fact, despite its infrequent occurrence, rollover accidents account for 33\% of all passenger vehicle fatalities. This highlights the need for a rollover prevention system in modern vehicles.

The aim of this thesis is to design an accurate yet computationally light rollover prevention controller. Previous research shows that measuring or estimating rollover state is incredibly difficult. Several methods exist, each with their own advantages and limitations. In this work, the novel Predictive Load Transfer Ratio (PLTR) is used to estimate rollover state. This rollover detection method has good accuracy and does not require special measuring equipment. Moreover, the PLTR has slight predictive properties as well. This is then coupled with a model predictive controller (MPC) in order to actively prevent rollover, as opposed to a more reactive feedback controller. The controller is designed using differential equations derived from a two-track vehicle model in order to keep it as computationally light as possible. A benchmark controller is also designed using the same MPC framework with another rollover detection method.

The controllers are then tested in a virtual environment, with the test vehicle performing the fishhook maneuver which induces rollover in the vehicle. Analysis from the results of the simulation show that the proposed rollover prevention controller has better rollover prevention properties compared to the benchmark controller while being more efficient as well. However, this comes at the cost of reference yaw rate tracking. Based on these results, future work can be done to merge this controller with an ESP system where it only activates in critical rollover scenarios.