M.H. Sangdani
Papers
2
Total Citations
46
H-Index
2
About
M.H. Sangdani is a robotics researcher whose work focuses on the intersection of intelligent control systems, optimization algorithms, and flexible-joint robotics. Their primary research areas include computed torque control, evolutionary optimization techniques, and parameter identification for robotic manipulators. Sangdani’s most impactful contribution is the development of a genetic algorithm-based optimal computed torque control strategy for vision-based tracker robots, published in 2017 and cited 42 times—a strong indicator of its influence in the field. This work uniquely combines simulation and experimental validation, demonstrating practical applicability. In a subsequent 2020 study, Sangdani applied particle swarm optimization (PSO) to parameter identification for a target tracker robot with flexible joints, specifically addressing the elastic behavior introduced by belt and pulley mechanisms. This research is notable for tackling the real-world challenge of joint flexibility, which often degrades tracking accuracy. Sangdani’s work is valuable for students and researchers interested in advanced control strategies, bio-inspired optimization, and the practical challenges of flexible robotic systems.
Research Focus
Key Achievements
Top Papers
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