Guo‐Ping Jiang
Papers
7
Total Citations
124
H-Index
5
About
Guo-Ping Jiang is a leading figure in intelligent robotics and mechatronic systems, with a focus on data-driven control, fault diagnosis, and specialized robotic design for infrastructure. His most impactful work, a 2023 study on “Data-Driven Control of Hydraulic Manipulators by Reinforcement Learning” (58 citations), pioneers the use of actor-critic reinforcement learning to achieve high-accuracy tracking control in complex 6-DOF hydraulic robotic arms, addressing a critical challenge in industrial automation. Jiang’s contributions extend to structural health monitoring, notably through the design of a helix cable-detecting robot for cable-stayed bridges (28 citations), which enables precise inspection of internal wire breaks—a vital safety innovation. He has also advanced error compensation in grinding robots (14 citations) and developed threshold algorithms for fault diagnosis in SCARA manipulators (9 citations), enhancing reliability in manufacturing. His recent work on MR dampers for climbing robots under wind loads (5 citations) and improved modeling for sheet metal bending (7 citations) underscores his versatility. With over 120 total citations, Jiang’s research bridges theoretical control methods and practical robotic applications, making him a key contributor to safer, smarter automation in challenging environments.
Research Focus
Key Achievements
Top Papers
- 1Data-Driven Control of Hydraulic Manipulators by Reinforcement Learning58 citations · 2023
- 2HELIX CABLE-DETECTING ROBOT FOR CABLE-STAYED BRIDGE: DESIGN AND ANALYSIS28 citations · 2014
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- 7Initial design and analysis of a helix cable detecting robot3 citations · 2013