Papers
23
Total Citations
330
H-Index
10
About
Yaobin Chen is a distinguished robotics and control systems researcher whose career has been defined by foundational contributions to the theory and application of optimal control for robotic manipulators. His work centers on two primary domains: minimum-time control (MTC) of robotic systems and neural network-based adaptive control, areas in which he has produced some of the field's most rigorously analytical results. Chen's most celebrated contributions lie in establishing the mathematical structure of minimum-time control laws for robotic manipulators. Through elegant applications of the Pontryagin minimum principle and Hamiltonian canonical formulations, he proved that optimal time control inherently requires at least one actuator to remain in saturation—a result of profound practical and theoretical significance. These findings, elaborated across single-arm, multi-arm, and path-constrained scenarios, have collectively garnered well over 200 citations and remain essential reading for researchers in trajectory optimization. His 2003 paper alone has earned 68 citations, reflecting lasting relevance. Beyond optimal control, Chen extended his expertise to neural adaptive control, proposing network-based controllers capable of handling manipulators with entirely unknown dynamics. His work on trajectory planning in Cartesian space further demonstrates a consistent commitment to bridging theoretical rigor with practical implementation, making his body of research invaluable to students and engineers navigating the complexities of modern robotics.
Research Focus
Key Achievements
Top Papers
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- 4Minimum-time control laws for robotic manipulators20 citations · 1993
- 5Time-optimal control of two-degree of freedom robot arms20 citations · 2003
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- 8A method for trajectory planning of robot manipulators in Cartesian space15 citations · 2002
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