Papers
5
Total Citations
105
H-Index
3
About
Haijing Wang is a rising leader in the field of safe and constrained robotic control, with a primary focus on high-order control barrier functions (HoCBFs) and their application to nonlinear and uncertain robotic systems. Her work addresses a critical challenge in modern robotics: ensuring safety and stability while respecting strict physical constraints on states, inputs, and outputs. Wang’s most influential contribution is the development of robust HoCBF-based optimal control methods, which prevent the chattering and loss of smoothness common in traditional constraint-satisfaction approaches. Her 2022 paper on impedance control with time-varying output constraints has garnered 67 citations, establishing a foundation for her subsequent innovations. In 2023, she advanced the field by integrating safety-stability perspectives into optimal control for constrained nonlinear systems. Most recently, Wang has pioneered neural network-augmented HoCBFs to handle uncertainties in robotic dynamics, enabling adaptive tracking control under input-output constraints. With over 100 total citations and a rapidly growing publication record, Haijing Wang is shaping the next generation of safety-critical robotic control, making her work essential reading for researchers in autonomous systems and nonlinear control theory.
Research Focus
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