Mohit Mehndiratta
Nanyang Technological University, Visual Components (Finland)
Papers
9
Total Citations
190
H-Index
7
About
Mohit Mehndiratta is a robotics and control systems researcher whose work sits at the intersection of model predictive control (MPC), learning-based control, and autonomous aerial robotics. His research has made significant strides in addressing one of the most persistent challenges in MPC design: the labor-intensive, trial-and-error process of weight tuning. His landmark 2018 paper on reinforcement learning-based automated MPC tuning (51 citations) pioneered a data-driven solution to this problem, while subsequent work exploring deep learning for self-tuning controllers continues advancing this vision. Mehndiratta has also developed innovative learning strategies for feedback linearization control of aerial robots operating in uncertain environments, with his robust tracking control framework earning 35 citations and demonstrating real-world applicability in package delivery drones. His use of Gaussian Process-based learning control for geological outcrop visualization (27 citations) showcases his ability to bridge robotics with geoscience applications. Beyond standard quadrotors, his work extends to over-actuated platforms, fault-tolerant control, and wind turbine inspection, reflecting a broad and applied research agenda. With a growing citation record exceeding 190 citations, Mehndiratta has established himself as a meaningful contributor to intelligent, autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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