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About

R.M. Sanner is a robotics and control systems researcher whose work sits at the intersection of adaptive control theory, neural network methodologies, and space robotics applications. Best known for pioneering the application of neural network-based adaptive control to complex robotic systems, Sanner has made significant contributions to solving the challenging problem of controlling robots operating under conditions of uncertainty and incomplete system modeling. His most influential work, "Adaptive control of free-floating space robots using neural networks" (2005, 28 citations), addresses a particularly difficult class of problems involving spacecraft-mounted manipulators, where traditional control approaches break down due to unknown mass properties. Complementing this, his research on structurally dynamic wavelet networks (2002, 24 citations) advanced the efficiency and accuracy of neurocontrol algorithms for poorly modeled robotic systems. Sanner has also demonstrated a commitment to translating theory into practice, contributing to the experimental development of a power-assisted space suit glove joint in collaboration with ILC Dover and the University of Maryland Space Systems Laboratory. His earlier work on multiresolution radial basis function networks further laid groundwork for modern adaptive robotic control architectures, making his research portfolio both theoretically rigorous and practically consequential.