M. Greene

University of Calgary, Auburn University

Papers

Total Citations

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About

M. Greene’s research lies at the intersection of surgical robotics, adaptive control, and neural network applications, with a focus on enhancing precision and adaptability in robotic systems. A key contribution is the performance evaluation of a surgical telerobotic system, where kinematic indices of the master hand-controller were used to assess and improve dexterity—work that has garnered 8 citations and informs the design of safer, more intuitive surgical tools. Greene also advanced adaptive control theory with a seminal 2002 study on indirect adaptive control of a two-link robot arm using regularization neural networks. This work introduced a dual-network control scheme: one network acts as a system identifier via a recursive algorithm, while the other provides proportional control, enabling the arm to compensate for flexibility and time-varying dynamics. With 6 citations, this approach demonstrated how neural networks can robustly handle nonlinearities in real-time robotic control. Greene’s contributions bridge theoretical control methods and practical robotic applications, offering foundational insights for students and researchers in mechatronics, biomedical engineering, and intelligent systems.