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Hesong Shen is a leading researcher in advanced control theory for robotic systems, with a primary focus on sliding mode control, disturbance rejection, and prescribed-performance tracking. His work addresses the critical challenge of controlling robot manipulators under uncertainties, backlash hysteresis, and external disturbances—conditions that often render traditional model-based approaches impractical. Shen’s most cited paper, “Recursive terminal sliding mode based control of robot manipulators with a novel sliding mode disturbance observer” (2023, 32 citations), introduces a robust framework that combines recursive sliding surfaces with a disturbance observer to achieve finite-time convergence and high precision. His 2021 paper on model-free adaptive sliding mode control (19 citations) proposes an innovative funnel-boundary method that eliminates the need for exact system dynamics, making it highly suitable for real-world applications. Shen also developed an adaptive neural sliding mode controller with prescribed performance (7 citations) to handle backlash hysteresis, and a prescribed-time integral sliding mode strategy (6 citations) using an adaptive super-twisting observer. His latest work (2025) extends robust backstepping control to strict-feedback nonlinear systems via fuzzy approximation. With over 65 total citations, Shen’s contributions are pivotal for advancing reliable, high-performance control in uncertain robotic environments.