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About

Rushikesh Kamalapurkar is a control systems researcher whose work spans nonlinear control theory, adaptive dynamic programming, neural network-based observers, and multi-robot systems. His early career made significant strides in robust control for uncertain nonlinear systems, most notably through his 2013 paper on saturated RISE feedback control, which developed a continuous control law for second-order nonlinear systems subject to bounded disturbances — a work that has garnered over 100 citations and remains a foundational reference in the field. Building on this, Kamalapurkar advanced the design of dynamic neural network-based observers for output feedback tracking, addressing the practical challenge of controlling systems when full state information is unavailable. His research extends into autonomous systems and robotics, including innovative work on target tracking under intermittent camera measurements and online approximate optimal path-following for mobile robots using adaptive dynamic programming techniques. More recently, he has tackled cooperative multi-robot manipulation problems, developing concurrent learning-based algorithms that enable teams of robots to estimate unknown payload dynamics in real time. Across his body of work, Kamalapurkar consistently bridges rigorous theoretical guarantees with practical autonomous systems applications, making his contributions valuable to both control theorists and robotics engineers.