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Scalable Algorithms for Physical Systems

We are seeking to develop more reliable algorithms for use with physical systems of varying dimensionality. Using these algorithms, we address issues of computational complexity and resource management in the design of algorithms for information determination, control, and sensitivity analysis which remain applicable to complicated nonlinear and impulsive systems. Our projects involve distributed control theory, hybrid control, sensitivity minimization, impacting systems, and information determination in continuous systems.

People 

Core Researchers

Todd Murphey
Malcom MacIver
Jarvis Schultz
Alex Ansari
Vlad Seghete
Lauren Miller
Tim Caldwell
Andrew Wilson
Ahalya Prabhakar

Collaborators

Lanny Smoot at Disney Research 
Magnus Egerstedt at Georgia Tech

Related publications

Mamakoukas, G., M. A. MacIver, and T. D. Murphey, "Feedback Synthesis for Controllable Underactuated Systems using Sequential Second Order Actions", Robotics: Science and Systems, 2017. Google Scholar

Ansari, A., and T. D. Murphey, "Sequential Action Control: Closed-Form Optimal Control for Nonlinear Systems", IEEE Transactions on Robotics, vol. 32, issue 5, pp. 1196 - 1214, Oct. 2016. Google Scholar

Mamakoukas, G., M. A. MacIver, and T. D. Murphey, "Sequential Action Control for Models of Underactuated Underwater Vehicles in a Planar Ideal Fluid", American Control Conference (ACC), Boston, MA, pp. 4500-4506, 07/2016. Google Scholar

Ansari, A., and T. D. Murphey, "Control-On-Request: Short-Burst Assistive Control for Long Time Horizon Improvement", American Control Conference (ACC), 2015. Google Scholar

Jochum, E., J. A. Schultz, E. Johnson, and T. D. Murphey, "Robotic Puppets and the Engineering of Autonomous Theater", Controls and Art: Springer International Publishing, pp. 107-128, 2014. Google Scholar

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