Dan Bao

Nanjing University of Science and Technology, National University of Singapore

Papers

Total Citations

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About

Dan Bao is a leading researcher in intelligent robotic systems, with a primary focus on adaptive control, neural network-based trajectory tracking, and motion reliability for robotic manipulators. His most influential work, "Adaptive Neural Trajectory Tracking Control for n-DOF Robotic Manipulators With State Constraints" (2022, 44 citations), introduces a groundbreaking control scheme that addresses critical challenges in robotic operations—parameter variations, unknown functions, and time-varying external disturbances. This contribution has become a foundational reference for researchers working on constrained robotic systems, offering a robust framework for ensuring precision and safety in complex environments. Bao further advances the field with his deep motion reliability scheme (2023, 11 citations), which integrates deep learning to enhance the dependability of robotic operations under uncertainty. His work bridges theoretical control theory and practical robotics, making significant strides toward more adaptive and resilient autonomous systems. With a growing citation impact, Bao’s research is shaping the next generation of intelligent manipulators, particularly in applications requiring high accuracy and safety, such as manufacturing and assistive robotics. His contributions are essential reading for students and engineers seeking to understand state-of-the-art adaptive control in robotics.