Papers
9
Total Citations
181
H-Index
6
About
Janine Matschek is a control systems researcher whose work sits at the intersection of model predictive control (MPC), robotics, and machine learning. Her research focuses primarily on nonlinear predictive control for path following, force control, and safe human-robot interaction — areas where precision and constraint satisfaction are critical. Matschek's most influential contribution is her work on predictive path-following control, distinguishing it from conventional trajectory tracking by decoupling geometric precision from timing constraints. Her 2018 survey on nonlinear predictive control for trajectory tracking and path following (49 citations) has become a key reference in the field. Building on this foundation, she extended MPC frameworks to incorporate force feedback and admittance control, enabling robots to follow exact paths while managing physical contact with uncertain environments — work demonstrated on both lightweight and industrial robots. More recently, Matschek has advanced learning-supported MPC, integrating Gaussian processes and multi-mode learning to handle system uncertainty with formal safety guarantees. Her 2023 paper on safe machine-learning-supported force and motion control (21 citations) reflects a growing commitment to deploying intelligent robotic systems safely alongside humans. Across her portfolio, Matschek has consistently bridged theoretical rigor with real-world implementation, making her research particularly valuable for robotics engineers and control theorists alike.
Research Focus
Key Achievements
Top Papers
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- 4Combined Predictive Path Following and Admittance Control25 citations · 2018
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- 6Multi-Mode Learning Supported Model Predictive Control with Guarantees17 citations · 2018
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