Keli Pang
Papers
5
Total Citations
80
H-Index
4
About
Keli Pang is a rising leader in advanced robotics control, specializing in adaptive and learning-based control for robotic systems operating under complex constraints. His research focuses on teleoperation, visual servoing, and parameter estimation, with a unifying theme of achieving high-performance control without relying on computationally heavy approximation functions. Pang’s most impactful work, a 2023 paper on prescribed performance control for teleoperation systems of nonholonomic mobile manipulators (34 citations), introduces a novel synchronization scheme that eliminates the need for approximation functions, enhancing efficiency in applications like space exploration and medical assistance. He has also made significant contributions to fixed-time and finite-time control, as seen in his 2022 paper on global composite learning fixed-time control (27 citations), which improves parameter convergence speed and system stability—a critical advancement over traditional steady-state approaches. His 2023 work on adaptive finite-time parameter estimation (9 citations) further refines this, enabling faster and more accurate estimates for constrained robots. With recent papers in 2024 addressing prescribed time error constraints and adaptive visual servoing with visibility and tracking constraints, Pang is pushing the boundaries of robot autonomy, ensuring robust performance under real-world limitations.
Research Focus
Key Achievements
Top Papers
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