Subash C.B. Gopinath
Papers
4
Total Citations
16
H-Index
2
About
Subash C.B. Gopinath is a researcher specializing in intelligent control systems, iterative learning control (ILC), and autonomous robotics. His work centers on developing advanced mathematical frameworks for improving trajectory tracking in robotic systems, with a particular focus on incorporating prior experience into learning control algorithms to overcome limitations of conventional approaches. Gopinath's most notable contributions involve applying orthogonal function series — including Fourier, associated Hermite, and wavelet series — to iterative learning control schemes for robot manipulators and mobile robots. His 2007 paper on fuzzy model-based experience inclusion in ILC stands as his most recognized work, accumulating 9 citations, and addresses a fundamental challenge in conventional ILC: the assumption of zero initial input for each new tracking task. By leveraging past trajectory knowledge, his controllers significantly reduce convergence time and improve overall control efficiency. His 2009 wavelet series-based learning controller extends these ideas to kinematic path-tracking in mobile robotics, demonstrating broad applicability across robotic platforms. While his citation counts remain modest, his research represents meaningful foundational contributions to intelligent and adaptive control theory, offering practical solutions for real-world robotic tracking problems that continue to inform subsequent work in autonomous systems and learning-based control.
Research Focus
Key Achievements
Top Papers
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