Zekun Zhang

Harbin Engineering University, Shandong University

Papers

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About

Zekun Zhang is a leading researcher in adaptive and learning-based robot control, with a focus on enabling industrial robots to perform complex, force-sensitive tasks with human-like compliance. His work centers on variable impedance control, set-membership adaptive identification, and composite learning strategies to overcome fundamental challenges in robotic manipulation. Zhang’s 2019 paper on efficient learning variable impedance control, with 12 citations, introduces methods for robots to modulate arm impedance in unstructured environments, allowing them to learn repetitive contact tasks without explicit programming. His 2022 study on set-membership adaptive control, cited 7 times, advances fast-converging identification algorithms by using bounded ellipsoid estimation to exploit prior system knowledge, reducing reliance on strict excitation conditions. In his 2023 work on composite learning exponential tracking, with 5 citations, Zhang addresses kinematic and dynamic uncertainties simultaneously, achieving parameter convergence and improved tracking under relaxed persistent excitation constraints. These contributions have significant implications for manufacturing, assembly, and human-robot collaboration, positioning Zhang as a key innovator in making industrial robots more adaptable, efficient, and capable in real-world environments.