Chandan Kumar Sah
Indian Academy of Sciences, Indian Institute of Science Bangalore
Papers
3
Total Citations
21
H-Index
2
About
Chandan Kumar Sah is a rising researcher at the intersection of control theory, robotics, and data-driven dynamical systems. His work focuses on developing robust, real-time algorithms for nonlinear and underdetermined systems—critical challenges in modern robotics and automation. Sah’s most cited paper (2022, 12 citations) introduces a novel zeroing neural network (ZNN) that enforces prescribed performance constraints on states and their derivatives, offering a principled solution for time-varying underdetermined linear systems. This work bridges theoretical guarantees with practical bounds, a key advance for safety-critical applications. More recently, his 2025 paper (8 citations) tackles the long-standing problem of linear embedding for nonlinear robotic control. By proposing an adaptive Koopman operator framework, Sah addresses the fragility of traditional data-driven methods, enabling more robust control synthesis. His 2024 overview paper (1 citation) further cements his role as a synthesizer of emerging paradigms, surveying data-driven identification and control techniques for robotics. With a growing citation trajectory and contributions that span from neural dynamics to operator-theoretic methods, Sah is establishing himself as a thoughtful architect of next-generation control systems—one who prioritizes both theoretical rigor and practical deployability.
Research Focus
Key Achievements
Top Papers
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