To increase the contribution of offshore wind energy to the global energy mix in an
economically sustainable manner, it is required to reduce the costs associated with the …

In this work, we present a novel, data‐driven, quality modeling, and control approach for
batch processes. Specifically, we adapt subspace identification methods for use with batch …

Direct data-driven control has attracted substantial interest since it enables optimization-
based control without the need for a parametric model. This paper presents a new …

Process complexities are characterized by strong nonlinearities, dynamics, and
uncertainties. Monitoring such a complex process requires a high-quality model describing …

In this work, we propose and develop data-driven explicit state-space based fault detection,
isolation and estimation filters that are directly identified and constructed from only the …

Modern software should satisfy multiple goals simultaneously: it should provide predictable
performance, be robust to failures, handle peak loads and deal seamlessly with unexpected …

In recent years, the amount of data available from systems has drastically increased,
motivating the use of direct data-driven control techniques that avoid the need of parametric …

As a useful and efficient alternative to generic model-based control scheme, data-driven
predictive control is subject to bias-variance trade-off and is known to not perform desirably …

One of the primary challenges of system identification is determining how much data is
necessary to adequately fit a model. Non-asymptotic characterizations of the performance of …

Due to advancing technology, systems in engineering are becoming increasingly complex
and interconnected. Despite the ubiquity of systems that can be modelled as interconnected …