Woodruff School of Mechanical Engineering
High Performance Control Using Low Performance Infrastructure
Dr. Todd Murphey
Thursday, February 5, 2015 at 3:00:00 PM
MARC Building, Room 114
Dr. Jonathan Rogers
Robotic applications require real-time control for high-dimensional, nonlinear/nonsmooth systems operating in an uncertain environment, often with limited actuation, poor quality sensors, and low bandwidth. Computational simulation tools have evolved in the last two decades to efficiently meet many of the associated needs, whereas computational control and estimation tools largely have not. This talk will focus on substantial progress towards bringing fully automated nonlinear control synthesis in software to robotics and other nonlinear applications. The first part of this talk will focus on the use of variational integrators in real-time, low-bandwidth systems. An example is an experimental system that uses sensing from a Kinect sensor for real-time, closed-loop nonlinear control in the Robot Operating System (ROS). The second part of the talk will be about how reformulating the control problem can lead to software that performs reliably for an array of nonlinear control systems. Specifically, there is a control problem that has an analytic feedback solution for general affine nonlinear systems. Moreover, it provides continuous-time control that is globally well-posed, inherits stability properties from classical linear techniques, and allows both control saturation and unilateral state constraints. Successful examples include many of the nonlinear benchmark systems used both in robotics and controls, including inversion of the cart-pendulum, the acrobot, the pendubot, and hopping loco-motion. Importantly, some of these examples can executed in real-time on a mobile phone running the Android operating system, indicating that real-time nonlinear control is feasible for many more systems than previously believed.
Dr. Todd D. Murphey is an Associate Professor of Mechanical Engineering at Northwestern University. He received his B.S. degree in mathematics from the University of Arizona and the Ph.D. degree in Control and Dynamical Systems from the California Institute of Technology. His laboratory is part of the Neuroscience and Robotics Laboratory, and his research interests include computational methods for mechanics and real-time optimal control, physical networks, and information theory in physical systems. Honors include the National Science Foundation CAREER award in 2006, membership in the 2014-2015 DARPA/IDA Defense Science Study Group, and Northwestern's Charles Deering McCormick Professorship of Teaching Excellence. He created the Coursera online class titled Everything Is The Same: Modeling Engineered Systems in 2013 and is a Senior Editor of the IEEE Transactions on Robotics.
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