Huan Chang

State Key Laboratory of Automotive Simulation and Control

Papers

Total Citations

H-Index

About

Huan Chang’s research lies at the intersection of robotic manipulation, nonlinear control, and human–robot interaction. Their most cited work introduces a nonlinear model predictive control (NMPC) framework that simultaneously addresses path following and terminal force regulation for robotic manipulators—a critical capability for tasks requiring both precision and compliance, such as assembly or surgical robotics. By embedding a virtual force dynamics into the control loop, Chang’s approach enables robots to maintain accurate geometric tracking while exerting controlled contact forces, bridging a gap between motion and force control. Though early in their career, with the 2020 paper accumulating 4 citations, the work signals a focused contribution to model-based control theory and its practical deployment. Chang’s research is particularly relevant for researchers working on autonomous manipulation in constrained environments, where safety and accuracy are paramount. Their ongoing efforts promise to advance the reliability of robots in real-world contact tasks, making them a rising voice in the field of robotic control systems.