Papers
54
Total Citations
2,143
H-Index
19
About
Yiming Jiang is a prominent robotics and control systems researcher whose work has fundamentally advanced the fields of adaptive control, neural network-based learning, and multi-arm robotic systems. His research centers on solving complex control challenges in robot manipulators, with particular emphasis on finite-time and fixed-time convergence, uncertain kinematics and dynamics, and intelligent learning-based control strategies. Among his most influential contributions, Jiang's 2018 work on adaptive parameter estimation with finite-time convergence (462 citations) revolutionized how robot systems identify unknown kinematic and dynamic parameters, prioritizing not just accuracy but convergence speed. His pioneering research on bimanual robot control (391 citations) addressed the formidable challenge of coordinating dual-arm systems grasping common objects with guaranteed global stability—a critical milestone for real-world collaborative robotics. His development of adaptive fuzzy control for dual-arm robots under deep uncertainty (325 citations) further cemented his reputation as a leader in intelligent robot control. Jiang has also contributed meaningfully to mobile robot motion planning, neural network control reviews, and agricultural harvesting robotics, demonstrating impressive breadth. With multiple highly cited publications spanning foundational theory and practical application, his cumulative impact exceeds 1,700 citations, making him an essential reference for researchers navigating modern intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10Review of Research Advances in Fruit and Vegetable Harvesting Robots40 citations · 2023