Selvaraj Gopinath
Papers
1
Total Citations
4
H-Index
1
About
Selvaraj Gopinath is a researcher in robotics and control systems, with a particular focus on advanced learning-based control strategies for industrial automation. His key research areas include iterative learning control, wavelet-based signal processing, and trajectory tracking for robotic manipulators. Gopinath’s major contribution lies in the innovative application of wavelet series approximations to design learning controllers that enhance the precision and efficiency of industrial robot manipulators. His most-cited work, "Wavelet series based iterative learning controller design for industrial robot manipulators" (2009), with 4 citations, demonstrates a novel approach to approximating desired and actual trajectories using finite wavelet coefficients, enabling more accurate and robust tracking control. This work highlights his ability to bridge mathematical signal processing with practical robotic control, offering a foundation for further advancements in adaptive and learning-based systems. Gopinath’s research is particularly valuable for students and engineers seeking to integrate wavelet theory into real-world automation challenges, showcasing how iterative learning can improve performance in repetitive tasks. His contributions underscore the potential of combining classical control methods with modern computational techniques to solve complex industrial problems.
Research Focus
Key Achievements
Top Papers
- 1