Surendra Kumar
Papers
7
Total Citations
44
H-Index
4
About
Surendra Kumar is a robotics and control systems researcher whose work centers on the intelligent control of robotic manipulators, adaptive neuro-fuzzy systems, and optimization-based control strategies. His research addresses one of the fundamental challenges in robotics: the inherent nonlinearity and dynamic complexity of robotic arms that make precise mathematical modeling difficult. Kumar's most cited contribution, "Puma 560 Optimal Trajectory Control Using Genetic Algorithm, Simulated Annealing and Generalized Pattern Search Techniques" (2008, 15 citations), demonstrates his expertise in applying evolutionary and metaheuristic optimization algorithms to fine-tune controllers for industrial robot arms. Complementing this, his work on Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers for three-link SCARA manipulators (10 citations) showcases his skill in deploying hybrid intelligent control approaches that overcome unmodeled dynamics in real-world systems. Across his body of work, Kumar has explored fuzzy reliability analysis for electric robots, radial basis function-based neuro-fuzzy controllers, and neural network applications for redundant manipulators, accumulating over 44 citations. His research bridges classical control theory and modern computational intelligence, offering practical solutions that span simulation environments and physical robotic platforms — making his work particularly valuable for graduate students and engineers working in robotics and automation.
Research Focus
Key Achievements
Top Papers
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- 4New Approach for Electric Robot Fuzzy Reliability Analysis5 citations · 2007
- 5Hybrid Ga Tuned Rbf Based Neuro-Fuzzy Controller For Robotic Manipulator4 citations · 2008
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- 7Conventional and Intelligent Controllers for Robotic Manipulator2 citations · 2006