Zhu Quanmin

Papers

Total Citations

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About

Zhu Quanmin is a researcher focused on advancing control theory and robotics, with key contributions in adaptive sliding mode control and neural network-based systems. Their most cited work, "Adaptive full-order sliding mode control of rigid robotic manipulators" (2015), introduces a robust control scheme that ensures precise position tracking for robotic arms despite system uncertainties, driving tracking errors to zero asymptotically. This work has garnered 5 citations, reflecting its foundational role in robotic manipulation. Zhu also pioneered a "RBF neural networks based robot non-smooth adaptive control" (2013), which combines radial basis function networks with non-smooth control laws to enhance stability and safety in robotic operations, earning 3 citations. Additionally, their innovative application of Hidden Markov Models to locate soccer robots (2015, 2 citations) demonstrates a novel probabilistic approach for predicting robot trajectories, laying groundwork for autonomous multi-agent systems. Zhu’s research bridges theoretical rigor with practical robotics challenges, offering solutions that improve precision, adaptability, and reliability in dynamic environments—a valuable resource for students and engineers exploring advanced control strategies.