Papers
7
Total Citations
283
H-Index
5
About
Haibo Ji is a control systems researcher whose work spans mobile robotics, adaptive control, and autonomous systems. His research is primarily focused on developing robust control strategies for nonholonomic wheeled mobile robots (WMRs) and aerial vehicles, addressing real-world challenges such as velocity constraints, input saturation, parametric uncertainties, and external disturbances. Ji's most influential contribution, "Adaptive-Neural-Network-Based Trajectory Tracking Control for a Nonholonomic Wheeled Mobile Robot with Velocity Constraints" (2020), has garnered over 160 citations, demonstrating the significant impact of his neural network-based approach to handling uncertain system dynamics. His trio of WMR-focused papers collectively highlights his expertise in combining adaptive sliding mode control, extended state observers, and neural networks to achieve reliable trajectory tracking under practical limitations. Beyond ground robotics, Ji has extended his research to aerial systems, exploring safety-critical control of quadrotors using control barrier functions and path integral methods, as well as flocking control for flying robots in three-dimensional environments. His work on image-based visual servoing (IBVS) with unknown dead-zone inputs further reflects his versatility across robotic platforms. Through his methodologically rigorous and application-driven research, Ji has established himself as a noteworthy contributor to intelligent and resilient robot control.
Research Focus
Key Achievements
Top Papers
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- 6Output feedback hierarchical control for nonlinear systems3 citations · 2017
- 7Flocking control of flying robots considering model's dynamics processes3 citations · 2017