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About

Orhan Aksoy is a control systems researcher whose work centers on the nonlinear control of robotic manipulators, with a particular emphasis on tendon-driven systems and Euler-Lagrange dynamics. His research addresses fundamental challenges in robotics: achieving precise trajectory tracking when systems face parametric uncertainty, limited sensor feedback, and unmeasurable states. Aksoy’s major contributions lie in developing inverse optimal adaptive output feedback controllers, which not only ensure asymptotic position tracking but also minimize a predefined cost function—a sophisticated approach that bridges optimality with robustness. His 2014 paper on nonlinear robust control of tendon-driven manipulators (16 citations) remains his most cited work, establishing foundational methods for this challenging class of robots. Aksoy has advanced the field by proposing variable structure and nonlinear filter-based observers that estimate unmeasured velocities and disturbances, enabling effective control with only position measurements. His work on adaptive partial state feedback for tendon-driven robots further demonstrates his commitment to practical, sensor-limited applications. Through these contributions, Aksoy has provided elegant theoretical frameworks that directly address real-world constraints in robotic control, making his research valuable for engineers designing high-performance, cost-effective robotic systems.