Gaussian Process Regression for Data-Driven Model Predictive Control

Systems and Control deals with modelling and control design of many different types of systems with different behaviour and characteristics. Often times it is challenging to build mathematical models to describe the system dynamics due to limited knowledge of the underlying system, complex nonlinearities, or the existence of unknown exogenous...

Deriving Timing Properties from System Traces using Data-driven Techniques

With the growth in the complexity of real-time embedded systems, there is an increasing need for tools and techniques to understand and compare the observed runtime behavior of a system with the expected one. Since many realtime applications require periodic interactions with the environment, one of the fundamental problems in guaranteeing...

Probabilistic Quantification of Airspace Resilience

The resilience of the Air Traffic Management (ATM) system to disturbances is required to maintain high operation performance. Before it can be improved, the resilience of the ATM system must be quantified. The measurement of resilience requires knowledge of a system reference state. This thesis proposes a novel methodology to detect disruptions...

Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework

A prevalent approach for learning a control policy in the model-free domain is by engaging Reinforcement Learning (RL). A well known disadvantage of RL is the necessity for extensive amounts of data for a suitable control policy. For systems that concern physical application, acquiring this vast amount of data might take an extraordinary amount...