Department of Information Technology

Systems and control

Modelling, analysis and control of dynamical systems.

About us

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Our everyday experience is governed by the world of systems and control. We are increasingly dependent on the purposeful, robust, safe and efficient functioning of dynamical systems around us. Systems and control deals with the analysis, design, and control of dynamical systems in order to respond in a specific manner. The properties of real systems typically evolves with time and the information that we can obtain from them through measured signals is uncertain. At the Division of Systems and Control, we investigate ways of developing mathematical models for dynamical systems and signals that reflect their main features and allow for powerful methods to extract information and act on this. We do research in this area with applications such as biomedicine, wireless communication, wastewater treatment, and navigation.

Research areas

Statistical Machine Learning

Many interesting phenomena around us are complex, dynamical and stochastic in nature, and the available data are inherently uncertain. We develop theory and tools for learning, reasoning and acting based on probabilistic models and measured data, methods that allow humans and machines to better understand the surrounding world. Contact person: Thomas Schön

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System Identification

Dynamical systems, physical and others, can be described by a variety of mathematical models. In some cases the model can be determined from first principles, but more often the model must be estimated based on measurements of how the system reacts to input signals. We develop methods for modeling and estimation of dynamical systems based on such input/output measurements. The models can be used for analysis, in order to better understand the properties of the system, or for control, to automatically regulate a process without human interaction. Contact person: Alexander Medvedev

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Automatic Control

Feedback control is the powerful concept of measuring the output of system in order to determine suitable input signals so that the system to be controlled behaves in a desirable way. The combination of this concept with the continuing improvement in computing power has led to a proliferation of control systems. We develop control and estimation theory and practical strategies for a variety of technical systems, for instance watertreatment plants, networked systems, biomedical systems and smart structures. Contact person: Bengt Carlsson

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Signal Processing

The goal of signal processing is to extract information from measured quantities, or signals. This broad concept ranges from simple linear filtering of time series to reduce noise, to nonlinear parameter estimation based on high-dimensional data using statistical models. Estimation theory, optimization, and statistics, play a central role. Contact person: Thomas Schön

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Important application areas

Biomedical systems

The theory and methods of dynamical systems, control, identification and signal processing have much to offer research and clinical practice in modern medicine. We develop methods used in the diagnosis, assessment, and treatment of medical conditions, based on dynamical models of physiological and biological systems. Currently we have projects on Parkinson's disease, breast cancer, diabetes and balance impairment. Contact person: Alexander Medvedev

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Wastewater engineering

Water quality and treatment of water is a growing concern around the world. Demands on quality and increasing loads call for optimized operation of wastewater treatment plants. Applied research in automatic control is an important tool in improving the performance of treatment plants. We are developing control and estimation strategies for wastewater treatment plants that improve pollutant removal, reduce the need for chemicals and yield energy savings. Contact person: Bengt Carlsson

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Updated  2017-04-17 13:40:43 by Kjartan Halvorsen.