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
Conference Contribution:
Malena Caparroz et al:
A new approach to autotuning PID controllers.
2025.
Conference Contribution:
Yde Sinnema, Martina Maggio:
Analysis of Control Systems Under Sensor Timing Misalignments.
2025.
Conference Contribution:
Volodymyr Voytenko, Pechorkin Yaroslav:
A small-sized information display system for entering a flight task to an unmanned aerial vehicle.
2025.
Conference Contribution:
Maksym Solodchuk, Volodymyr Voytenko:
Software and hardware complex 2DCAM for physical simulation of two-coordinate positioning system of additional UAV video camera.
December 2025.
Conference Contribution:
Volodymyr Voytenko et al:
Simulation of the Two-Coordinate Positioning System of the UAV Auxiliary Video Camera.
2025.
PhD Thesis:
Jonas Hansson:
Scalable Control Design for Networked Systems : Coordination Through Local Cooperation.
April 2025.
Journal Article:
Jonas Hansson, Emma Tegling:
Compositional design for time-varying and nonlinear coordination.
2025.
Journal Article:
Italo C. Fantozzi et al:
Guiding organizations in the digital era : Tools and metrics for success.
2025.
Journal Article:
Monica Sileo et al:
A Novel Decentralized Leader–follower Control Scheme for Centroid and Formation Tracking.
2025.
Journal Article:
Jian Zhou et al:
Robust Predictive Motion Planning by Learning Obstacle Uncertainty.
2025.