Bryan Brenner

Missouri University of Science and Technology

Papers

Total Citations

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About

Bryan Brenner is a robotics and control systems researcher whose work sits at the intersection of neural networks, optimal control theory, and multi-robot coordination. His research focuses primarily on developing intelligent formation control strategies for nonholonomic mobile robots, leveraging machine learning techniques to solve complex, real-world coordination challenges. Brenner's most influential contribution, "Neural Network-Based Optimal Control of Mobile Robot Formations With Reduced Information Exchange" (2012), has garnered 64 citations and stands as a landmark study in the field. This work introduced a novel leader-follower formation control framework that significantly reduces inter-robot communication requirements — a critical practical consideration for real-world deployments — while maintaining robust performance through nonlinear optimal control approximations. Building upon this foundation, his earlier papers from 2011 tackled the challenging infinite-horizon optimal tracking control problem, solving it online and forward-in-time using both discrete-time formulations and linearly parameterized neural networks. These works demonstrate a consistent research trajectory: making optimal control computationally tractable and communication-efficient for autonomous robot teams. Brenner's cumulative contributions offer valuable tools for researchers developing scalable, intelligent multi-robot systems across applications ranging from search and rescue to autonomous logistics.