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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Effect Inference From Two-Group Data With Sampling Bias
. In IEEE Signal Processing Letters, volume 26, number 8, pp 1103-1106, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019. (DOI). -
Specialized Interior-Point Algorithm for Stable Nonlinear System Identification. In IEEE Transactions on Automatic Control, volume 64, number 6, pp 2442-2456, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019. (DOI).
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RFI Mitigation for UWB Radar Via Hyperparameter-Free Sparse SPICE Methods. In IEEE Transactions on Geoscience and Remote Sensing, volume 57, number 6, pp 3105-3118, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019. (DOI).
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Identification of continuous Volterra models with explicit time delay through series of Laguerre functions. In , 2019.
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Modeling of human smooth pursuit by sparse Volterra models with functionally dependent parameters. In , 2019. Publication status: Submitted
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Networked delay control for 5G wireless machine-type communications using multiconnectivity. In IEEE Transactions on Control Systems Technology, volume 27, number 4, pp 1510-1525, 2019. (DOI).
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Loading factor estimation under affine constraints on the covariance eigenvalues with application to radar target detection. In IEEE Transactions on Aerospace and Electronic Systems, volume 55, number 3, pp 1269-1283, 2019. (DOI).
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Hybrid observers for systems with intrinsic pulse-modulated feedback. Ph.D. thesis, Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology nr 1812, Acta Universitatis Upsaliensis, Uppsala, 2019. (fulltext
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Monotonicity and robustness in Wiener disorder detection. In Sequential Analysis, volume 38, number 1, pp 57-68, 2019. (DOI, Fulltext).
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Volterra modeling of the human smooth pursuit system in health and disease. Licentiate thesis, IT licentiate theses / Uppsala University, Department of Information Technology nr 2019-003, Uppsala University, 2019. (fulltext).
