Lukas Hewing

Papers

Total Citations

259

H-Index

About

Lukas Hewing is a leading researcher in data-driven control and robotics, whose work bridges the gap between advanced control theory and practical robotic systems. His primary research areas include model predictive control (MPC), trajectory tracking, and variable stiffness actuation. Hewing’s most significant contribution is his pioneering work on data-driven MPC for robotic manipulation, as demonstrated in his highly cited 2019 paper (235 citations), which introduces a framework for achieving high-precision trajectory tracking in compliant and cost-effective robotic arms—a critical advancement for industrial and service robotics. This work addresses the challenge of accurate positioning without relying on expensive, stiff hardware, instead leveraging learned models and real-time optimization. Additionally, Hewing has made notable contributions to the control of variable stiffness actuators, exploring closed-loop optimal control (20 citations) and robust gain-scheduled methods (4 citations) to enhance safety and energy efficiency in human-robot interaction. His research is widely recognized for its practical impact, enabling safer, more adaptable robots. Hewing’s achievements underscore his role as a key figure in advancing data-driven control for next-generation robotic systems.