M. Steinegger
Papers
3
Total Citations
7
H-Index
2
About
M. Steinegger is a robotics researcher whose work focuses on trajectory planning, iterative learning control, and modular control architectures for reconfigurable robotic systems. His major contributions include developing a trajectory planning method for manipulators that optimally concatenates linear-quadratic (LQ) control primitives, enabling efficient and smooth motion for kinematic chains. He also advanced the practical implementation of norm-optimal iterative learning control (ILC) on programmable logic controllers (PLCs), bridging the gap between theoretical control algorithms and industrial automation. Additionally, Steinegger introduced a distributable control framework for reconfigurable robots, using dynamic programming to solve inverse kinematics in a distributed manner while incorporating an extra control input to reduce errors. Although his citation counts are modest—with his most cited paper, on trajectory planning, receiving three citations—his work demonstrates a strong emphasis on bridging theory and application in robotic control. His research is particularly relevant for students and engineers interested in industrial robotics, modular systems, and real-time control implementation.
Research Focus
Key Achievements
Top Papers
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