Zongli Lin
Papers
10
Total Citations
139
H-Index
6
About
Zongli Lin’s research lies at the intersection of optimal control, multi-robot coordination, and reinforcement learning, with a focus on real-world robotic systems. His most influential work introduces a reinforcement learning approach for the optimal control of a two-wheeled self-balancing robot, achieving 49 citations by eliminating the need for precise system models. Lin has also made foundational contributions to control theory, including methods to smooth discontinuities in bounded continuous feedback laws (27 citations), improving convergence performance under input constraints. In multi-robot systems, he developed distributed control algorithms for containment and group dispersion behaviors, validated experimentally with dynamic leaders, and established constrained motion models for mobile robots that support target detection and dynamic coverage. His work on consensus in discrete-time multi-agent systems with nonlinear rules and time-varying delays (13 citations) further demonstrates his impact on distributed control. Lin’s achievements include pioneering the use of state space models for multi-robot coordination and advancing stochastic game-theoretic approaches for smart manufacturing. With a career spanning theoretical advances and experimental validation, his research continues to shape autonomous robotics and intelligent control systems.
Research Focus
Key Achievements
Top Papers
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- 2On improving the performance with bounded continuous feedback laws27 citations · 2002
- 3Constrained Motion Model of Mobile Robots and Its Applications18 citations · 2009
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