Research at Systems and Control
Modeling, inference and control in complex dynamical systems
Current projects
Dynamical systems, physical and others, can be described by a variety of mathematical models. We develop methods for modeling and estimation of dynamical systems based on measurements of input/output data. 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 MedvedevAutomatic control and System identification
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 SchönStatistical Machine Learning
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 central roles. Contact person: Thomas SchönSignal Processing
Important application areas
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 MedvedevBiomedical systems
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 CarlssonWastewater engineering
Recent publications
More comprehensive list of publications
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Non-parametric time-domain tremor quantification with smart phone for therapy individualization
. In IEEE Transactions on Control Systems Technology, volume 28, 2020. (DOI). Publication status: Epub ahead of print
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Joint Axis Estimation for Fast and Slow Movements Using Weighted Gyroscope and Acceleration Constraints. In , 2019. (fulltext:postprint
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Visualization of convolutional neural network class activations in automated oral cancer detection for interpretation of malignancy associated changes. In 3rd NEUBIAS Conference, Luxembourg, 2-8 February 2019, 2019.
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Control-Engineering Perspective on Deep Brain Stimulation: Revisited. In , 2019. (DOI).
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Automated active fault detection in fouled dissolved oxygen sensors. In Water Research, volume 166, PERGAMON-ELSEVIER SCIENCE LTD, 2019. (DOI).
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Deep convolutional networks in system identification. In Proc. 58th Conference on Decision and Control, IEEE, 2019.
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Flexible Models for Smart Maintenance. In 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), IEEE International Conference on Industrial Technology, pp 1772-1777, 2019.
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Decoupling multivariate polynomials for nonlinear state-space models. In IEEE Control Systems Letters, volume 3, number 3, pp 745-750, 2019.
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Deep learning applied to system identification: A probabilistic approach. Licentiate thesis, IT licentiate theses / Uppsala University, Department of Information Technology nr 2019-007, Uppsala University, 2019. (fulltext).
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Evaluation of a sensor algorithm for motor state rating in Parkinson's disease. In Parkinsonism & Related Disorders, volume 64, pp 112-117, ELSEVIER SCI LTD, 2019. (DOI).