Papers
16
Total Citations
1,435
H-Index
13
About
Derong Liu is a leading figure in adaptive dynamic programming (ADP), reinforcement learning, and intelligent control, with a particular focus on event-triggered and safe control systems. His major contributions include pioneering event-triggered decentralized tracking control for modular reconfigurable robots, where he developed neural network observers to enable efficient, localized decision-making. He also advanced event-triggered optimal neuro-controllers for unknown nonlinear systems, significantly reducing computational load while maintaining performance. Liu’s work on safe reinforcement learning integrates barrier functions into cost functions, enabling autonomous systems to avoid obstacles adaptively—a critical step toward real-world deployment. His most cited paper, “Neural Information Processing” (2017), has garnered 695 citations, reflecting his foundational influence in the field. Additionally, his research on multi-agent consensus control for manipulators with kinematic uncertainties has been widely recognized (84 citations). Liu has also edited influential special issues on deep reinforcement learning and ADP, shaping the direction of modern intelligent control. With over 1,300 citations across his top works, his contributions continue to inspire advances in autonomous systems, robotics, and adaptive optimal control.
Research Focus
Key Achievements
Top Papers
- 1Neural Information Processing695 citations · 2017
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- 7Neural Information Processing35 citations · 2017
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- 9Neural Information Processing22 citations · 2016
- 10Particle Swarn Optimized Adaptive Dynamic Programming21 citations · 2007