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About

Jun-Wei Zhu is a leading researcher in robotics and intelligent control systems, with a primary focus on dynamic identification, fault-tolerant control, and fault reconstruction for industrial and mobile robots. His most impactful work, a 2021 paper on dynamic identification of industrial robots using a nonlinear friction model and a novel LS-SOS algorithm, has garnered 62 citations, establishing a robust framework for accurate parameter estimation in complex robotic systems. Zhu’s contributions extend to pioneering reinforcement learning-based model-free adaptive fault-tolerant control for flexible multi-joint manipulators, as demonstrated in his 2023 study on the Baxter robot (16 citations), which addresses the challenging issues of nonlinearity and coupling in fault scenarios. He has also advanced mobile robot safety through a performance-guaranteed fault reconstruction method employing a two-dimensional gain-regulation mechanism (12 citations), and explored sensor attack reconstruction via a switching Kalman fusion mechanism. Notably, his work integrates intermediate estimators with model predictive control for fault-tolerant tracking in wheeled mobile robots, enhancing real-time performance under actuator faults. With a growing citation impact and a focus on practical, resilient robotic systems, Zhu’s research is instrumental in developing safer, more autonomous robots for industrial and service applications.