Faculty Directory
Todd Murphey

Charles Deering McCormick Professor in Teaching Excellence

Associate Professor of Mechanical Engineering


2145 Sheridan Road
Tech B286
Evanston, IL 60208-3109

847-467-1041Email Todd Murphey


Neuroscience and Robotics Lab (NxR)


Mechanical Engineering


Master of Science in Robotics Program

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Ph.D. Control and Dynamical Systems, California Institute of Technology, Pasadena, CA

B.S. Mathematics (summa cum laude), University of Arizona, Tucson, AZ

Research Interests

Professor Murphey's research focuses on computational methods in dynamics and control.  The group focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control.  Much of the work uses structured integration (numerical techniques specifically suited to mechanical systems) to ensure stability and robustness of the numerical technique.  Example projects include simulation and control synthesis for a biomechanical model of the human hand, real-time control of robotic systems using structured integration, stabilization of large-scale power network models using a single bit of control authority, mechanical simulation of rat whiskers, numerical modeling of contact in locomotion, and dynamic search for underwater vehicles using electrosense for localization and mapping.   His group's work on robotic marionettes has been featured at the Museum of Science and Industry, Chicago.

Significant Recognition

  • National Science Foundation CAREER award (2006)
  • Defense Science Study Group 2014-2015

Significant Professional Service

  • Associate Editor for IEEE Transactions on Automation Science and Engineering
  • Editor for IEEE Transactions on Robotics

Selected Publications


    E. Tzorakoleftherakis, F. Mussa-Ivaldi, R. Scheidt, and T. D. Murphey, “Effects of optimal tactile feedback in balancing tasks: a pilot study,” in American Controls Conf. (ACC), 2014.


    V. Seghete and T. D. Murphey, “Uniqueness conditions for simultaneous impact in locomotion: existence, uniqueness, and design consequences,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 1, pp. 154–168, 2014.


    L. Miller and T. D. Murphey, “Simultaneous optimal estimation of mode transition times and parameters applied to simple traction models,” IEEE Transactions on Robotics, vol. 29, no. 6, pp. 1496–1503, 2013.


    Y. P. Leong and T. D. Murphey, “Feature localization using kinematics and impulsive hybrid optimization,” IEEE Transactions on Automation Science and Engineering, vol. 10, no. 4, pp. 957 – 968, 2013.

    T. Caldwell and T. D. Murphey, “Single integration optimization of linear time-varying switched systems,” IEEE Transactions on Automatic Control, vol. 57, no. 6, pp. 1592–1597, 2012.

    Y. Silverman, L. Miller, M. MacIver, and T. D. Murphey, “Optimal planning for information acquisition,” in IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2013.

    A. D. Wilson and T. D. Murphey, “Optimal trajectory design for well-conditioned parameter estimation,” in IEEE Int. Conf. on Automation Science and Engineering (CASE), 2013.

In the Classroom

Professor Murphey developed the Coursera Massive Open Online Course (MOOC) "Everything Is the Same: Modeling Engineered Systems" and had over 18,000 students enroll in Autumn, 2013. The course was based on one of the core undergraduate classes in systems analysis (EA3), a class he has innovated through development of classroom experiments. Moreover, he has developed the ME 314 Machine Dynamics course, focusing on the application of variational analysis to simulation and design of mechanisms. He has additionally developed ME 454, an introduction to numerical methods in optimal control. In all these courses, Professor Murphey focuses on project-based learning.