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About

K. Merve Dogan is a robotics and control systems researcher whose work centers on advanced control strategies for robot manipulators and multi-agent systems, with a particular focus on learning control, adaptive control, and output feedback methods. Her most influential contribution, "Learning Control of Robot Manipulators in Task Space" (2017, 16 citations), addresses a critical practical challenge: enabling robotic end-effectors to reliably execute repetitive industrial tasks despite unknown or uncertain system dynamics. Building on this foundation, her earlier work developed repetitive learning controllers that guarantee asymptotic tracking of periodic trajectories in operational space, and observer-based output feedback frameworks that eliminate the need for direct velocity measurements — a significant advancement for real-world deployment where full state information is rarely available. Beyond single-robot systems, Dogan has expanded her research into networked and modular robotic systems, developing distributed adaptive control architectures that coordinate multiple manipulators under uncertainty, and experimentally validating cooperative behaviors in mobile robot networks. Her more recent investigations into actuator deficiencies and control effectiveness uncertainties demonstrate a commitment to engineering-relevant robustness. With a growing body of work accumulating over 40 citations, Dogan's research offers both theoretical rigor and practical applicability, making her a noteworthy contributor to intelligent robotics and control engineering.