Papers
1
Total Citations
4
H-Index
1
About
Daniel Burk’s research lies at the intersection of distributed control systems, optimization algorithms, and open-source robotics. His most significant contribution is the development and experimental validation of GRAMPC-D, an open-source framework for distributed model predictive control (DMPC). This framework achieves millisecond-range solutions to the Alternating Direction Method of Multipliers (ADMM) algorithm, enabling real-time coordination of multi-agent systems. Burk demonstrated the practical viability of GRAMPC-D by deploying it on the remotely accessible Robotarium testbed, a landmark achievement that bridges the gap between theoretical control algorithms and real-world implementation. His work has garnered attention within the control systems community, with his key paper accumulating citations that underscore its impact on advancing accessible, scalable DMPC solutions. By open-sourcing his framework, Burk has empowered researchers and engineers to implement distributed control without requiring specialized hardware, democratizing access to cutting-edge multi-agent coordination. His contributions are particularly valuable for applications in robotics, smart grids, and autonomous vehicle platooning, where real-time distributed decision-making is critical.
Research Focus
Key Achievements
Top Papers
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