Promises of Deep Kernel Learning for Control Synthesis

Deep Kernel Learning (DKL) combines the representational power of neural networks with the uncertainty quantification of Gaussian Processes. Hence, it is potentially a promising tool to learn and control complex dynamical systems. In this letter, we develop a scalable abstraction-based framework that enables the use of DKL for control...

Integral Support Structure and Controller Design of an Offshore Wind Turbine

In the past decade, the wind energy subsidies provided by the Dutch government is declining. In order to adopt wind energy as a well-established source of renewable energy, the costs of (offshore) wind energy should be lowered. This can be achieved by increasing the energy production or by lowering the maintenance and initial costs. In search of...