Qi-Jie Zhou

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About

Qi-Jie Zhou has made foundational contributions to the control of robotic systems, particularly in the challenging domain of constrained motion and force regulation. His work addresses a critical problem in robotics: how to simultaneously control a manipulator's position and the force it applies when contacting a surface, all while compensating for unknown system parameters. In his most-cited paper (37 citations), Zhou developed an adaptive control algorithm that exploits the unique structure of constrained dynamics, enabling stable and precise interaction with rigid environments—a key capability for tasks like assembly, grinding, or surgical assistance. He further advanced the field by integrating variable structure control with input-output linearization (11 citations), providing rigorous stability guarantees for bounded tracking in nonlinear systems. Zhou also explored neural network-based approaches for force control (9 citations), anticipating the growing role of learning in robotics. His work bridges classical adaptive and sliding-mode methods with modern intelligent control, offering practical solutions for robots that must safely and accurately interact with the physical world. For students and researchers, Zhou’s research remains a cornerstone in understanding how to achieve robust, compliant manipulation under uncertainty.