The design of a control algorithm is difficult when models are unavailable, the physics are
varying in time, or structural uncertainties are involved. One such case is an oil production …

In oil production platforms, processes are nonlinear and prone to modeling errors, as the
flow regime and components are not entirely known and can bring about structural …

Steady-state process models are common in virtual flow meter applications due to low
computational complexity, and low model development and maintenance cost …

The present work investigated the use of an echo state network for a gas lift oil well. The
main contribution is the evaluation of the network performance under conditions normally …

Advanced control systems are becoming more and more sophisticated every day. For safety-
critical systems such as chemical processes, nuclear reactors, aircraft and spacecraft, the …

La pile à combustible à membrane échangeuse de protons est un convertisseur
électrochimique prometteur pour la production électrique à partir du vecteur hydrogène …

As technology advances over time, data-driven approaches become more relevant in many
fields of both academia and industry, including process control. One important kind of …

Networked nonlinear systems consist of several subsystems that interact with one another.
Since real-world systems are seldom isolated, networked systems represent a considerable …

In this thesis we propose a novel class of deep Recurrent Neural Networks (RNNs) explicitly
extending the Reservoir Computing framework to the Deep Learning paradigm. Thereby, we …

Recurrent Neural Networks (RNN) tend to be costly to optimize, though they posess desir-
able properties for dynamic system identification and serve as an universal approximator for …