Abstract: Neuromuscular Electrical Stimulation (NMES) is prescribed by clinicians to aid in the recovery of strength, size, and function of human skeletal muscles to obtain physiological and functional benefits for impaired individuals. The two primary applications of NMES include: 1) rehabilitation of skeletal muscle size and function via plastic changes in the neuromuscular system, and 2) activation of muscle to elicit movements that result in functional performance (i.e., standing, stepping, reaching, etc.) termed functional electrical stimulation (FES). In both applications, stimulation protocols of appropriate duration and intensity are critical for preferential results. Automated NMES methods hold the potential to maximize the treatment by self-adjusting to the particular individual (facilitating potential in-home use and enabling positive therapeutic outcomes from less experienced clinicians). Yet, the development of automated NMES devices is complicated by the uncertain nonlinear musculoskeletal response to stimulation, including difficult to model disturbances such as fatigue. Unfortunately, NMES dosage (i.e., number of contractions, intensity of contractions) is limited by the onset of fatigue and poor muscle response during fatigue. This talk describes recent advances and experimental outcomes of control methods that seek to compensate for the uncertain nonlinear muscle response to electrical stimulation due to physiological variations, fatigue, and delays.
Prof. Warren Dixon received his Ph.D. in 2000 from the Department of Electrical and Computer Engineering from Clemson University. After completing his doctoral studies he was selected as an Eugene P. Wigner Fellow at Oak Ridge National Laboratory (ORNL). In 2004, Dr. Dixon joined the University of Florida in the Mechanical and Aerospace Engineering Department, where he currently is the Charles Taylor Faculty Fellow and holds a University of Florida Research Foundation Professorship. Dr. Dixon’s main research interest has been the development and application of Lyapunov-based control techniques for uncertain nonlinear systems. He has published 3 books, an edited collection, 9 chapters, and over 250 refereed journal and conference papers. His work has been recognized by the 2011 American Society of Mechanical Engineers (ASME) Dynamics Systems and Control Division Outstanding Young Investigator Award, 2009 American Automatic Control Council (AACC) O. Hugo Schuck Best Paper Award in the Application category, 2006 IEEE Robotics and Automation Society (RAS) Early Academic Career Award, an NSF CAREER Award (2006-2011), 2004 DOE Outstanding Mentor Award, and the 2001 ORNL Early Career Award for Engineering Achievement.
He currently serves as a member of the U.S. Air Force Science Advisory Board and as the Director of Operations for the Executive Committee of the IEEE CSS Board of Governors. He is currently or formerly an associate editor for ASME Journal of Journal of Dynamic Systems, Measurement and Control, Automatica, IEEE Transactions on Systems Man and Cybernetics: Part B Cybernetics, and the International Journal of Robust and Nonlinear Control.
Distinguished Lecture Program
Talk Title: Theoretical and Experimental Outcomes of Closed-Loop Neuromuscular Control Methods to Yield Human Limb Motion
Talk Title: Concurrent Learning-Based Adaptive Dynamic Programming for Autonomous Agents
Analytical solutions to the infinite horizon optimal control problem for continuous time nonlinear systems are generally not possible because they involve solving a nonlinear partial differential equation. Another challenge is that the optimal controller includes exact knowledge of the system dynamics. Motivated by these issues, researchers have recently used reinforcement learning methods that involve an actor and a critic to yield a forward-in-time approximate optimal control design. Methods that also seek to compensate for uncertain dynamics exploit some form of persistence of excitation assumption to yield parameter identification. However, in the adaptive dynamic programming context, this is impossible to verify a priori, and as a result researchers generally add an ad hoc probing signal to the controller that degrades the transient performance of the system. This presentation describes a forward-in-time dynamic programming approach that exploits the use of concurrent learning tools where the adaptive update laws are driven by current state information and recorded state information to yield approximate optimal control solutions without the need for ad hoc probing. A unique desired goal sampling method is also introduced as a means to address the classical exploration versus exploitation conundrum. Applications are presented for autonomous systems including robot manipulators, underwater vehicles, and fin controlled cruise missiles. Solutions are also developed for networks of systems where the problem is cast as a differential game where a Nash equilibrium is sought.