Linköping University is looking for a PhD student in Automatic Control to be admitted to the WASP Graduate School at the Department of Electrical Engineering (ISY).
The Division of Automatic Control offers a vibrant, internationally oriented research environment with a strong balance between theory and real-world applications. The division conducts high-quality research, doctoral education, and undergraduate teaching in key and rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion.
Research at the division is characterized by close integration of methodological development and practical applications. We maintain extensive collaborations with industrial partners as well as with leading research groups worldwide, providing doctoral students with excellent opportunities for international exposure and impactful research.
As a PhD student, you will join an open, collaborative, and intellectually stimulating environment that values scientific curiosity, independence, and teamwork. More information about the division is available at: https://liu.se/en/organisation/liu/isy/rt.
For more information about working at ISY, please visit: https://liu.se/en/article/open-positions-at-isy.
Your work assignments
This PhD project focuses on making advanced control methods, specifically Model Predictive Control (MPC), feasible for highly resource-constrained platforms such as nano-drones. The research will develop new adaptive and data-driven approaches – along with supporting theory – that enable MPC to run reliably on limited hardware by adjusting computational effort in real time. It will combine statistical performance guarantees with flexible “anytime” optimization strategies that adapt to available resources and mission priorities. The methods will be validated experimentally on nano-drone platforms (e.g., Crazyflie), pushing the limits of onboard computation while maintaining safety and performance. The overall goal is to bring high-performance, reliable control to low-cost autonomous systems operating under strict computational constraints.
