Control Seminar

High Precision Coordination and Motion Control via iterative Learning Control

Kira BartonAssistant ProfessorUniversity of Michigan, Department of Mechanical Engineering

In today’s engineering world, many emerging applications ranging from manufacturing to the autonomous vehicle industry require coordination of multiple systems. Traditional approaches for controlling these systems often neglect the underlying coupling in the application. To stay at the forefront of these fields requires the development of innovative approaches to new challenges. The research in this talk focuses on designing novel control strategies for cooperative applications. Electrohydrodynamic jet (E-jet) printing, an example of an emerging micro/nano-manufacturing process with applications in biotechnology and flexible electronics, requires multiple systems to work in a coordinated manner to achieve a desired objective. Repetitive execution of processes such as E-jet can be harnessed to achieve high performance. Iterative learning control (ILC) is an adaptive control technique for improving process performance in systems that execute a task repetitively. This research simultaneously exploits the repetitiveness and inherent coupling of the desired outcome by applying a coordinated ILC approach to processes such as E-jet. The versatility of this approach will be demonstrated through applications ranging from robotics to emerging manufacturing processes.
Kira Barton ( received her B.S. degree in Mechanical Engineering from the University of Colorado at Boulder in 2001. Barton continued her education in mechanical engineering at the University of Illinois at Urbana-Champaign and completed her M.S. and Ph.D. degrees in 2006 and 2010, respectively. She held a postdoctoral research position at the University of Illinois from Fall 2010 until Fall 2011, at which point she joined the Mechanical Engineering Department at the University of Michigan at Ann Arbor. Her primary research focus is on precision coordination and motion control for emerging applications, with a specialization in iterative learning control. Barton’s work intersects controls and manufacturing and combines innovative manufacturing processes with enhanced engineering capabilities. The potential impact of this research ranges from building DNA sensors for biological applications to controlling the coordination of satellites in space.

Sponsored by

Bosch, Eaton, Ford, GM, Toyota, Whirlpool and the MathWorks