Papers
31
Total Citations
1,526
H-Index
20
About
Dechao Chen is a prominent robotics and control systems researcher whose work centers on neural dynamics, motion planning, and intelligent control of robot manipulators. His research has made significant strides in addressing long-standing challenges in robotic control, particularly through the development of zeroing neural-dynamics (ZND) frameworks and adaptive optimization schemes. Chen's landmark 2017 paper on Jacobian-matrix-adaption for robot tracking control, which has garnered over 220 citations, redefined how unknown robotic models can be handled without relying on precise system parameters. His equally influential work on Robust Zeroing Neural-Dynamics (149 citations) introduced disturbance-rejection capabilities into neural network models, a critical advancement for real-world deployment. Chen has consistently pushed the boundaries of redundant manipulator control, proposing multi-objective and multi-level optimization schemes that simultaneously address obstacle avoidance, joint-physical constraints, and trajectory precision. His contributions extend to parallel robots, mobile manipulators, and dynamic path planning for mobile robots, demonstrating remarkable breadth. With a body of work accumulating over 1,000 citations, Chen has established himself as a leading voice in intelligent robotic control and optimization-driven motion planning research.
Research Focus
Key Achievements
Top Papers
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- 3A Hybrid Multi-Objective Scheme Applied to Redundant Robot Manipulators99 citations · 2015
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