Abhijit Singh

National Institute of Technology Durgapur

Papers

Total Citations

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About

Abhijit Singh is a researcher whose work sits at the intersection of robotics, control systems, and soft actuation. His primary focus is on developing intelligent control strategies for complex, nonlinear actuators, with a particular emphasis on Pneumatic Artificial Muscles (PAMs)—a class of soft actuators prized for their compliance and power-to-weight ratio in robotic and industrial applications. Singh’s most cited paper, “Neural Network-Based Gain Scheduled Position Control of a Pneumatic Artificial Muscle” (2022, 4 citations), tackles the fundamental challenge of PAMs: their inherent nonlinearities and hysteretic behavior, which make precise modeling and control notoriously difficult. By integrating neural networks with gain scheduling, he proposes a robust framework that adapts to these complexities, offering a pathway toward more reliable and responsive soft robotic systems. Though early in his career, his work addresses a critical bottleneck in the field—bridging the gap between theoretical control methods and real-world actuator performance. Singh’s contributions are particularly valuable for researchers and engineers seeking to deploy PAMs in applications ranging from rehabilitation robotics to industrial automation, where precision and adaptability are paramount.