Weiran Wang

University of California, Merced, Jiangsu University of Science and Technology

Papers

Total Citations

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About

Weiran Wang’s research lies at the intersection of machine learning and underwater robotics, with a focus on low-dimensional regression and autonomous control systems. In his early work, Wang introduced a nonlinear regression method using auxiliary coordinates to handle high-dimensional inputs and outputs, offering a principled way to reduce dimensionality before mapping—a contribution that has garnered 6 citations and remains relevant for modern statistical learning. More recently, Wang has tackled the challenges of underwater vehicle manipulator systems (UVMS), where he developed a multi-motor synchronization control strategy based on a virtual shaft to address speed and load imbalances in random interference environments. His 2021 paper on this topic, also with 6 citations, demonstrates practical solutions for robust underwater operation. Additionally, Wang proposed a grasping control method for UVMS that fuses visual image enhancement to overcome weak illumination and multidisturbance issues. His work bridges theoretical regression techniques with real-world robotic applications, making him a notable figure in advancing autonomous underwater systems.