Pavankumar Tallapragada
Papers
3
Total Citations
8
H-Index
2
About
Pavankumar Tallapragada’s research lies at the dynamic intersection of control theory, robotics, and intelligent automation. His work is distinguished by a focus on event-triggered and adaptive control strategies that reduce computational and communication overhead while maintaining high performance. In his early influential work, “Lyapunov Based Sampling for Adaptive Tracking Control in Robot Manipulators: An Experimental Comparison” (2013, 4 citations), Tallapragada pioneered methods that use Lyapunov functions to determine when to sample and update control signals, significantly improving efficiency in robotic manipulation. More recently, his 2024 paper “Reinforcement Learning Aided Sequential Optimization for Unsignalized Intersection Management of Robot Traffic” (2 citations) tackles the complex, real-time problem of coordinating multiple robots arriving randomly at intersections. By framing this as a mixed integer program and integrating reinforcement learning, he offers a scalable solution for safe, optimal trajectory planning. His 2023 work on “Event-Triggered Polynomial Control for Trajectory Tracking by Unicycle Robots” (2 citations) further advances the field by introducing polynomial control inputs between events, minimizing approximation errors. Tallapragada’s contributions are shaping the next generation of resource-aware, autonomous systems, making his research essential reading for those interested in efficient, intelligent robot coordination.
Research Focus
Key Achievements
Top Papers
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