Sangram Redkar

Sangram Redkar is an Associate Professor at Arizona State University whose research focuses on nonlinear dynamics and robotics, with a strong emphasis on data-driven and learning-based control methods for complex mechanical systems. His work explores advanced control strategies for nonlinear and underactuated robotic platforms, including bio-inspired robots such as worm and caterpillar robots, as well as autonomous underwater vehicles and MEMS-based systems. A central theme of his research is the application of Koopman operator theory to enable interpretable, data-driven modeling and control of nonlinear dynamics, often combined with adaptive, robust, and fractional-order control techniques. His research integrates deep neural networks, system identification, and control theory to reduce chattering, improve robustness, and enhance real-time performance in robotic systems. He has published extensively in journals such as Nonlinear Dynamics, Neural Computing and Applications, Expert Systems with Applications, Robotica, Ocean Engineering, and International Journal of Control, and his work includes both theoretical contributions and experimental validation. His research also extends to biomedical and human-centered applications, including prosthetics, physiological signal processing, and fatigue assessment, reflecting an interdisciplinary approach to robotics and nonlinear control.