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Automatic Control

The Department of Automatic Control at Lund University was created in 1965 and has today grown to hosting about 60 people.

We give courses within the regular engineering program to students from different areas of engineering. We also have a PhD program where the students specialize in various theories and applications of automatic control.

Our research is concentrated to seven areas:
Modeling and Control of Complex Systems, Control and Real-Time Computing, Process Control, Robotics, Automotive Systems, Biomedical Projects and Tools.

The department is hosting several large research projects funded by the European Commission and Swedish funding agencies. There is also active collaboration with industry.

Positions for PhD students are announced here, usually around April 1 and October 1.

Recent Publications

Journal Article:
Marcus Greiff et al: Target Localization and Circumnavigation with Integral Action in R2. 2022.

Conference Contribution:
Alexandre Martins, Karl Erik Arzen: Dynamic Management of Multiple Resources in Camera Surveillance Systems*. May 2021.

Journal Article:
Claudio Mandrioli, Martina Maggio: Testing Self-Adaptive Software with Probabilistic Guarantees on Performance Metrics : Extended and Comparative Results. 2021.

Conference Contribution:
Marcus Greiff et al: Tuning and Analysis of Geometric Tracking Controllers on SO(3). May 2021.

Conference Contribution:
Marcus Greiff et al: Attitude Control on SU(2) : Stability, Robustness, and Similarities. May 2021.

Conference Contribution:
Pauline Kergus: Data-driven control of infinite dimensional systems : Application to a continuous crystallizer. May 2021.

Journal Article:
Yan Jiang et al: Dynamic Droop Control in Low-Inertia Power Systems. 2021.

Journal Article:
Giacomo Como, Gustav Nilsson: On the well-posedness of deterministic queuing networks with feedback control. 2021.

Conference Contribution:
Per Skarin, Karl-Erik Årzén: Explicit MPC recovery for cloud control systems. December 2021.

Journal Article:
F. García-Mañas et al: Experimental evaluation of feedforward tuning rules. 2021.