A Multi-Step Gaussian Process Learning Framework for Long-Horizon Vehicle Dynamics Prediction

This work introduces a novel training strategy for Gaussian Process (GP) models aimed at improving their predictive accuracy and uncertainty quantification capabilities over extended prediction horizons. This improvement is highly relevant for applications in model predictive control (MPC) in the autonomous driving domain. Learning-based MPC...

Online optimization and learning for the optimal power flow problem with unknown objectives

The Optimal Power Flow (OPF) problem, a cornerstone of power system operations, has gained increased attention since its inception by Carpentier in 1962. OPF is fundamentally an optimization challenge aimed at enhancing electric power system operations within the bounds of physical and operational constraints. Over the decades, various...

Wrinkle contraction direction: a useful feature for learning robotic fabric manipulation from demonstration

Deformable objects manipulation (DOM) is largely considered an open problem in robotics. The complexity stems from the high degrees of freedom and nonlinear nature of the object configurations. In this thesis, we consider placing and flattening tasks for cloth-like objects. We propose a practical framework to place a cloth on a surface based on...

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...

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...

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...

Field inversion and machine learning in turbulence modeling

Turbulence closure models will continue to be necessary in order to perform computationally affordable simulations in the foreseeable future. It is expected that Reynolds-averaged Navier-Stokes (RANS) turbulence models will still be useful with the further development of the more accurate, but computationally expensive large eddy simulation (LES...