Omkar Sudhir Patil
Papers
5
Total Citations
30
H-Index
4
About
Omkar Sudhir Patil is an emerging researcher whose work sits at the dynamic intersection of adaptive control theory, deep learning, and multi-agent systems. His research is primarily focused on developing intelligent, learning-based control frameworks for uncertain and nonlinear systems — an area of growing importance as autonomous systems become increasingly prevalent in real-world applications. Patil's most significant contributions involve bridging classical control-theoretic tools with modern machine learning architectures. His work on accelerated gradient methods for deep neural network-based adaptive control (2024, 10 citations) extends Nesterov's optimization techniques to handle nonlinear system uncertainties in real time — a meaningful advancement over prior methods limited to linear uncertainty structures. Complementing this, his Lyapunov-Based LSTM adaptive observer (2023, 8 citations) creatively integrates recurrent neural network memory capabilities with rigorous stability guarantees, advancing the frontier of data-driven state estimation. Beyond single-system control, Patil has made notable strides in multi-agent coordination, including indirect herding of agents with unknown interaction dynamics and distributed target tracking without full state information. Collectively accumulating over 30 citations across recent publications, his trajectory signals a researcher poised to make lasting contributions to intelligent, provably stable autonomous systems.
Research Focus
Key Achievements
Top Papers
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