Competence Centre for Advanced BioProduction by Continuous Processing
Motivation. During the last decades there have been impressive advances in control theory, offering new means for industry to meet the challenges of today’s society. For example, nonlinear and economic MPC together with distributed networked control opens up for large improvements in efficiency and flexibility in process industry. However, technology shifts in this type of industry is notoriously difficult, involving huge investments and training of a wide palette of personell, not to mention the business side.
Learning dynamical systems is an area closely related to machine learning, cyber-physical systems as well as real-time big data analytics, and it provides backbone algorithms for digitalization of industry and society. Among others, it is core technology in autonomous systems with applications such as smart buildings, self-driving vehicles, and self-learning robots. In this project we focus on three key themes: Fundamental techniques concerns learning parsimonious models in a statistical and computationally efficient way.
New directions in learning dynamical systems.
Smart feed design for biopharmaceutical production
Motivation. During the last decades there have been impressive advances in control theory, offering new means for industry to meet the challenges of today’s society. For example, nonlinear and economic MPC together with distributed networked control opens up for large improvements in efficiency and flexibility in process industry. However, technology shifts in this type of industry is notoriously difficult, involving huge investments and training of a wide palette of personell, not to mention the business side.