Papers
6
Total Citations
174
H-Index
5
About
Erlong Kang is a leading researcher in the control of uncertain robotic manipulators, with a focus on advanced model predictive control (MPC) and event-triggered strategies. His work addresses critical challenges in robotic systems operating under model uncertainty, input constraints, and environmental disturbances. Kang’s most influential contribution is his event-triggered MPC framework with learning terminal cost, which has garnered 70 citations for its novel integration of adaptive predictive models using radial basis function networks. He has also developed neural network-based MPC for tracking control (56 citations) and adaptive dynamic event-triggered output feedback methods, significantly improving robustness and efficiency in robotic manipulation. Notably, his research extends to high-performance assembly in extreme environments, such as space manipulator-assisted module docking, showcasing practical applications in aerospace. With over 160 total citations, Kang’s work has advanced the field of robotic control, offering solutions that balance computational efficiency with real-time performance. His achievements include pioneering sliding mode-based adaptive tube MPC for state-constrained systems and distributed event-triggered synchronization for teleoperation, demonstrating his impact on both theoretical and applied robotics.
Research Focus
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