Control of Dynamical Systems via Deep Kernel Learning

In the field of Systems and Control, optimal control problem-solving for complex systems is a core task. The development of accurate mathematical models to represent these systems’ dynamics is often difficult. This complexity comes from potential uncertainties, complex non-linearities, or unknown factors that might affect the system. Because of...

Data-driven optimal control for safe quadrotor navigation in windy environments

Creating autonomous Micro Aerial Vehicles for executing complex missions poses various challenges, including safe navigation in the presence of external wind disturbances. Most current navigation methods handle external wind disturbances through real-time estimation and rejection algorithms in the control stage, but lack safety guarantees in...

Learning-Based Model Predictive Control for a Wind-Powered Fresh Water Production Plant with Integrated Electricity Production

Growth of the population, industry and agriculture increases the demand for fresh water. However, with only a small portion of the water on earth being suitable for direct human use and consumption, this growth makes water scarcity an increasing problem. Seawater desalination is an effective method to counter such water stress. Reverse Osmosis ...

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...

Design of a reconfigurable metamaterial with the use of Bayesian optimization and machine learning

Metamaterials are a relatively new group of materials whose behaviour strongly depends on the design of their internal structure. They can be employed in a wide range of applications, one of which is presented in this thesis. As cardiovascular diseases account for around 30% of deaths worldwide the research done in the field of Materials Science...

Surrogate Constitutive Models with Multi-fidelity Gaussian Processes for Composite Micromodels

Various engineering applications rely on efficient, high performance materials to overcome design challenges. This high performance can be achieved by engineering micro-heterogenous materials also known as composites. Since the behavior of composites relies heavily on micro-scale interactions between different components, modeling...

Safe Reinforcement Learning Applications

Reinforcement Learning (RL) focuses on maximizing the returns (discounted rewards) throughout the episodes, one of the main challenges when using it is that it is inadequate for safety-critical tasks due to the possibility of transitioning into critical states while exploring. Safe Reinforcement Learning (SafeRL) is a subset of RL that focuses...

Gaussian Process Models for Preliminary Low-Thrust Trajectory Optimization

Low-thrust trajectories can benefit the search for propellant-optimal trajectories, but increases in modeling complexity and computational load remain a challenge for efficient mission design and optimization. An approach for developing models utilizing Gaussian Process (GP) regression and classification is proposed to perform computationally...

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...

Data - driven design of a metamaterial unit cell using sparse Gaussian processes

Metamaterials are a new class of materials where the properties crucially depend on the design of the unit cell that is periodically repeated in space. In this study a new metamaterial unit cell concept has been proposed, inspired by a class of space structures known as deployable masts. The ability of these structures to contract to a fraction...