Thesis (m/f/d): Learning-Based Control Allocation For Morphing UAVs

Dlr; Braunschweig; On-site; EUR 12,000 - 19,000; Full time

Job summary

The Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) invites students to work on a Master thesis about learning-based control allocation for morphing-wing UAVs in Braunschweig.

The project combines reinforcement learning, adaptive control and safety-critical design in aerospace systems. Supervised by DLR and international collaborators, the thesis emphasizes model uncertainty handling, safety filters, and feasibility certificates.

Qualifications

• Enrolled in a Master’s program in Computer Science, Control Engineering, Robotics, Aerospace Engineering or a related field.
• Hands-on experience with reinforcement learning is mandatory, ideally with policy-gradient methods such as PPO or SAC.
• Practical experience with Gymnasium or comparable RL environments is mandatory.
• Good understanding of control engineering specifically related to flight control design is a plus.
• Strong Python programming skills.
• Independent, structured way of working and a strong interest in combining RL, control and safety-critical autonomous systems.

Responsibilities

• Review learning-based methods for safe and feasible control allocation under model uncertainty.
• Compare uncertainty-handling approaches (adaptive, observer-based, set-based, statistical learning) by their guarantees, assumptions and real-time capability.
• Develop a learning-based control-allocation concept for a trailing-edge morphing-wing UAV under model uncertainty.
• Integrate safety mechanisms (e.g., CBF-based safety filters) and strategies to keep the allocation feasible (backup strategies, slack variables, feasibility certificates).
• Train and benchmark the developed approaches under varying operating conditions and model uncertainties.
• Analyze stability, safety and allocation feasibility.

Skills

Reinforcement learning

Policy gradients

Python programming

RL environments

Education

Master’s program in CS / Controls / Robotics / Aerospace

Tools

Gymnasium

Job description

Thesis (m/f/d): Learning-Based Control Allocation For Morphing UAVs

Place of work: Braunschweig, Cochstedt Starting date: Februar/März 2027 Career level: Student research project and final thesis Type of employment: Full-time, Part time Duration of contract: 6 Monate bzw. nach Prüfungsordnung

Remuneration: Remuneration is in accordance with the Collective Agreement for the Public Sector - Federal Government (TVöD-Bund).

Hint: Eligibility for security clearance and the willingness to undergo a security check pursuant to §§ 8 ff. of the Security Clearance Act (SÜG) are conditions for employment at DLR.

Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e. V.; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our12,000employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge?Then this is your place!

Welcome to the Institute of Flight Systems. Our work focuses on the interaction between aircraft configuration, pilots and modern flight system technology. From flight dynamics to unmanned aerial vehicles, from simulation to real flight tests - we analyse, test and develop innovations that will shape the flying of the future.

What to expect

This thesis develops a safe, learning-based control-allocation approach for a trailing-edge morphing-wing UAV under model uncertainty. The allocation must meet the primary control objectives while exploiting actuator redundancy for secondary goals. A central part is handling model uncertainty, e.g., via adaptive methods, disturbance observers, set-based methods or statistical learning. Another focus is allocation feasibility, covering backup strategies, slack variables, adaptive weighting, parameter optimization and explicit feasibility certificates. The thesis is jointly supervised by DLR and Prof. Mayank Shekhar Jha (CRAN, CNRS, Université de Lorraine, France).

Your tasks

• Review learning-based methods for safe and feasible control allocation under model uncertainty
• Compare uncertainty-handling approaches (adaptive, observer-based, set-based, statistical learning) by their guarantees, assumptions and real-time capability
• Develop a learning-based control-allocation concept for a trailing-edge morphing-wing UAV under model uncertainty
• Integrate safety mechanisms (e.g., CBF-based safety filters) and strategies to keep the allocation feasible (backup strategies, slack variables, feasibility certificates)
• Train and benchmark the developed approaches under varying operating conditions and model uncertainties
• Analyze stability, safety and allocation feasibility

Your profile

• Enrolled in a Master’s program in Computer Science, Control Engineering, Robotics, Aerospace Engineering or a related field
• Hands-on experience with reinforcement learning is mandatory, ideally with policy-gradient methods such as PPO or SAC
• Practical experience with Gymnasium or comparable RL environments is mandatory
• Good understanding of control engineering specifically related to flight control design is a plus
• Strong Python programming skills
• Independent, structured way of working and a strong interest in combining RL, control and safety-critical autonomous systems

We offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 6793) please contact:

Mark Spiller
Tel.: [phone]