Vehicles are becoming increasingly complex and the resource-demanding due to the developments aimed at driving autonomy. Viewing an automobile as a cyber-physical system (CPS), this work applies the advances of controls applied to CPS for improving resource-consumption. One such idea is that of event-based control. Theoretical developments in event-based control show promising results, but they require practical corroboration. Combining these aspects, an event-triggered vehicle stability controller is designed and tested on a real-time hardware-in-loop setup.

In this thesis we investigate the possibilities for applying Koopman methods for reachability analysis. Reachability analysis is a verification process used to determine that a dynamical system starting in an initial set X0 cannot reach a certain set of dangerous states D within a time interval [0,T]. Koopman methods seem promising, because they predict nonlinear behaviour using linear techniques. However they have not been widely applied to reachability analysis.

We describe three different Koopman methods: data-driven, Polyflow and Carleman. We use the Polyflow method combined with ideas from several other methods to create a new reachability tool: PolyReach. Next, we analyse the performance of PolyReach by comparing it with a state-of-the-art reachability algorithm Flow* on various nonlinear systems. Finally, we summarize the strengths and weaknesses of the PolyReach tool and discuss ideas for further improvement.

Advancements in knowledge and technology have led to an evolution of greenhouse operations: from simple transparent shelters to (one of) the most profitable sectors in agricultural industry. The understanding of the physiological and biological processes of the inside micro-climate and crop, as well as the introduction of climate control, were critical for improving both the quality and production rate. Advanced control strategies like MPC involve the application of horticultural and plant physiological knowledge using mathematical models that describe the dynamical behavior of the greenhouse and crop. An on-line control method balances the benefits of crop yield and costs of the climate control equipment. The increasing production and cultivation rate in response to the growing demand is at a point where the focus and tension is shifted to the energy input and environmental footprint of greenhouse operations. This stimulates the agricultural industry to provide sustainable solutions. An innovative greenhouse is developed by BBBLS Solutions, that uses a soap bubble cavity which improves the thermal properties and energy efficiency. The concept is in development phase and is acknowledged that there is potential for improved climate control and automation of the control of the soap bubble cavity. This research introduces a MPC strategy for controlling the greenhouse climate and the soap bubble cavity. The greenhouse processes are described by a mechanistic model, which simulates the inside climate variables as a function of the greenhouse structure, outside environmental conditions and climate control equipment. The dynamics of the soap bubble cavity and the corresponding thermal varying properties were then augmented in the climate model. The augmented model is used as a predictor for the future dynamics of the greenhouse climate, for which the optimizer generates a control input sequence for the climate control equipment and soap bubble generators inside the cavity. The MPC implementation results demonstrate that the greenhouse temperature can be controlled within the pre-defined bounds and that the filling of the soap bubble cavity is with respect to outside conditions and priorities. Furthermore, the MPC strategy supports real-time control as the CPU time can be less than the time between control instances. The proposed methodology is an initial attempt but proves to be a promising concept for further development.