Improving model-based control of a soft robot via gaussian process regression

Soft robots have the potential to accelerate robotiza- tion in areas that are complex and impractical for hard robots. The use of soft materials results in a safe and flexible design that is unattainable for hard robots. However, this attribute results in the need for new control approaches and strategies. Hybrid controllers are a relative...

Predicting noise attenuation level in the earplugs using Gaussian Process Regression

The earplug development followed by ALPINE, a hearing protection company is a trial and error method. This method leads to high material wastage. It is also a time-consuming and expensive development process. So, the objective of this thesis is to ease the error earplug development process by implementing a prediction model. The research is...

Contour Method with Uncertainty Quantification: A Robust and Optimised Framework via Gaussian Process Regression

Background: Over the past 20 years, the Contour Method (CM) has been extensively implemented to evaluate residual stress at the macro scale, especially in products where material processing is involved. Despite this, insufficient attention has been devoted to addressing the problems of input data filtering and residual stress uncertainties...

Forecasting of electric vehicle load profiles: Predicting the uncontrolled load stemming from a large group of electric vehicles as input for trading on energy markets

Exploiting electric vehicles for maintaining a stable electricity grid is a promising opportunity, which is recognized by both the academic and industrial world. Although the concept is relatively simple, the operating space containing many different stakeholders, rules and legislation is not. There are various practical issues that complicate...

Uncertainty Modelling in Aircraft Trajectory Predictions

Several initiatives are being developed to shift the current paradigm in Air Traffic Management (ATM) from the tactical-based approach to more strategic-based coordination of flights. This transformation of the ATM system relies on the improvement of predictive models that predict the 4D-trajectory of an aircraft. Previous studies primarily...

Predicting movable bridge deck expansion: A machine learning and asset management perspective

Movable bridge decks experience critical expansion in summer, leading to uncertainty and unpredictability in its availability doe to improper docking and safety hazard. If the bridges are not cooled soon, the inertia of expansion stays, causing prolongation of availability problems. Structural health monitoring of such bridges with a predictive...

Wind Profile Estimation from Aircraft Derived Data Using Kalman Filters and Gaussian Process Regression

Accurate wind information is crucial in air traffic management, for instance, to improve trajectory predictability and precision in controlled time of arrival. Nowadays, air traffic management relies on Numerical Weather Prediction, which usually has a low resolution and low update rate. A potential approach for improving the resolution and...

Quantifying Accuracy and Uncertainty in Data-Driven Flight Trajectory Predictions with Gaussian Process Regression

Several initiatives are developed to shift the current paradigm in Air Traffic Management from the tactical-based approach to more strategic-based coordination of flights. This transformation of the ATM system relies on the improvement of predictive models for the 4D flight trajectories. A variety of performance-based and data-driven approaches...