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About

Subhan Khan is a robotics researcher whose work centers on autonomous systems, model predictive control, and real-time perception for field robotics. His most impactful contribution, "Design and experimental validation of a robust model predictive control for the optimal trajectory tracking of a small-scale autonomous bulldozer" (34 citations), demonstrates a practical, validated approach to controlling heavy machinery in unstructured environments—bridging the gap between theoretical control methods and real-world deployment. Khan also addresses the computational constraints of autonomous systems in "Real-time 3D object proposal generation and classification using limited processing resources" (7 citations), tackling the challenge of perception on embedded platforms. Earlier work on "Performance Analysis of PID and State-Feedback Controller on the Depth Control of a Robotic Fish" (5 citations) shows his versatility in bio-inspired robotics, where he linearized nonlinear dynamics for control design. His recent exploration of "Stein Movement Primitives for Adaptive Multi-Modal Trajectory Generation" (2 citations) pushes beyond traditional Gaussian assumptions in learning from demonstration, aiming for more flexible robot skill acquisition. Across these projects, Khan consistently focuses on making autonomous systems robust, efficient, and deployable in the real world.