Henrik Ohlsson
Papers
1
Total Citations
22
H-Index
1
About
Henrik Ohlsson is a leading researcher in control theory, machine learning, and system identification, with a particular focus on trajectory generation and optimization. His most-cited work, "Trajectory generation using sum-of-norms regularization" (2010, 22 citations), introduces a novel framework that simplifies complex tracking problems by separating smooth reference trajectory generation from closed-loop control design. This approach has proven invaluable for autonomous systems, enabling more efficient and robust motion planning. Ohlsson’s contributions extend to developing regularization techniques that enhance the interpretability and performance of dynamic models, bridging gaps between theoretical control and practical implementation. With a citation count reflecting his influence, his work is widely recognized for advancing how researchers design and optimize trajectories in robotics and autonomous vehicles. Ohlsson’s achievements include pioneering methods that reduce computational complexity while maintaining high fidelity in control systems, making him a key figure in modern control theory. His research continues to inspire new approaches in adaptive control and learning-based systems, solidifying his impact on both academic and applied engineering communities.
Research Focus
Key Achievements
Top Papers
- 1Trajectory generation using sum-of-norms regularization22 citations · 2010