Hantao Huang

Papers

Total Citations

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About

Hantao Huang is a researcher whose work lies at the intersection of robotics, intelligent control systems, and neural network applications. His most cited paper, "Robust neural network–based tracking control and stabilization of a wheeled mobile robot with input saturation" (2018, 44 citations), addresses a critical challenge in mobile robotics: achieving both precise trajectory tracking and stable posture control under real-world constraints like input saturation, parametric uncertainties, and external disturbances. Huang’s key contribution is a novel error-state transformation scheme that enables a single neural network-based controller to handle both tracking and stabilization simultaneously—a problem traditionally requiring separate solutions. This work has been influential in advancing robust control for autonomous wheeled robots, particularly in applications demanding high reliability under physical limitations. Beyond this flagship study, Huang’s research portfolio explores intelligent control strategies that bridge theoretical rigor with practical implementation, making his findings valuable for engineers developing autonomous vehicles, service robots, and industrial automation systems. His work demonstrates how neural networks can provide adaptive, robust solutions to complex nonlinear control problems, earning recognition from peers working at the frontier of robotics and control theory.