Priyanka Bhagat
Papers
1
Total Citations
2
H-Index
1
About
Priyanka Bhagat is a robotics and control systems researcher whose work focuses on intelligent adaptive control for robotic manipulators operating in uncertain environments. Her most-cited paper, "Adaptive Fuzzy Self-learning Controller for Robotic Manipulators" (2006), introduces a novel control algorithm that combines a fixed controller with an adaptive fuzzy controller to achieve precise trajectory tracking despite dynamic changes and environmental uncertainty. This hybrid approach allows robotic manipulators to self-learn and adjust in real time, addressing critical challenges in automation and industrial robotics. While her citation count of 2 reflects a specialized, early-career contribution, the work demonstrates foundational thinking in merging fuzzy logic with adaptive control—a technique that has since gained traction in advanced robotics. Bhagat’s research is particularly relevant for students and engineers exploring robust control strategies for systems where traditional models fail, such as in manufacturing, surgical robotics, or space exploration. Her contribution underscores the importance of self-learning mechanisms in achieving reliable, autonomous robotic performance.
Research Focus
Key Achievements
Top Papers
- 1Adaptive Fuzzy Self-learning Controller for Robotic Manipulators2 citations · 2006