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Real-Time Optimization Strategies for Predictive Motion Control in Space Robotics
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Real-Time Optimization Strategies for Predictive Motion Control in Space Robotics
24.07.2026, Studentische Hilfskräfte, Praktikantenstellen, Studienarbeiten
Master’s Thesis / Internship Opportunity
Master’s Thesis / Internship
Real-Time Optimization Strategies for Predictive Motion Control
in Space Robotics (f/m/x)
Space robotics is an emerging field of growing relevance for applications
such as on-orbit servicing, on-orbit assembly, and active debris removal.
In these domains, robotic motion and interaction take place in highly
constrained environments and under strict computational and safety
requirements. Recent research has shown that Model Predictive Control
(MPC) can provide perfromant motion control while explicitly
accounting for constraints and uncertainties.
Robotic control problems are inherently nonlinear. Therefore, applying
Nonlinear Model Predictive Control (NMPC) in real time requires an efficient
formulation of the optimal control problem, together with a carefully
selected discretization and solution strategy. Previous work has
investigated an offline optimization procedure for identifying the most
effective discretization methods. This thesis will investigate
convexification methods to improve runtime performance on
space-representative hardware while maintaining satisfactory closed-loop
performance.
Your Contribution
We are seeking a motivated master’s student with an interest in space
robotics to implement an onboard model predictive controller.
Your Tasks
Implement an existing NMPC controller on space-representative real-time
hardware.
Investigate successive convexification strategies for nonlinear
operational constraints.
Derive bounds on the model mismatch introduced by convexification.
Benchmark runtime performance and closed-loop behavior on real-time
hardware.
Your Qualifications
Currently enrolled in a master’s program in aerospace engineering,
mechatronics, computer science, robotics, mathematics, or a related field.
Strong programming skills in C/C++ ; experience with
real-time or embedded implementation is a plus.
Working knowledge of MATLAB or Python is preferred.
Strong interest in space robotics or spacecraft dynamics.
Familiarity with Model Predictive Control and numerical optimization is
an advantage.
We Offer
The opportunity to work on a real-world research problem relevant to
autonomous space missions.
Collaboration within a leading research institute in space robotics.
Insight into robust model predictive control for safety-critical systems.
This thesis is ideal for students interested in embedded optimization,
numerical simulation, and space applications who enjoy translating
advanced algorithms into efficient, high-performance implementations.
Supervisors: Peter Kötting and Roberto Lampariello
Kontakt:
[email protected],
[email protected]
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