Jingyue Wang

Ford Motor Company (United States), University of Science and Technology Beijing

Papers

Total Citations

H-Index

About

Jingyue Wang’s research lies at the intersection of robotics, control systems, and bio-inspired locomotion, with a focus on enabling robots to navigate complex environments with greater autonomy and adaptability. In their most-cited work, Wang developed an online adaptive model predictive control (MPC) framework for robot driver speed control, integrating regularized least squares system identification with a Kalman filter to handle time-varying vehicle dynamics in real time. This contribution, published in 2018, has garnered 4 citations and addresses a critical challenge in autonomous driving: maintaining stable, responsive speed control under changing conditions. Earlier, Wang explored bio-inspired robotics through gait planning for a gecko-like robot transitioning from ground to wall surfaces. By combining theoretical degree-of-freedom analysis with ADAMS simulation, Wang designed a novel locomotion mode that enables stable climbing transitions—a foundational step toward robots capable of vertical movement. While citation counts remain modest, Wang’s work demonstrates a thoughtful progression from biologically inspired mechanical design to advanced, data-driven control strategies, reflecting a commitment to bridging theory and practical robotic performance.