Title: Optimization-based iterative learning control for robotic manipulators
Authors: Steinhauser, Armin
Pipeleers, Goele
Swevers, Jan
Issue Date: 22-Mar-2016
Conference: Benelux Meeting on Systems and Control edition:35 location:Soesterberg, The Netherlands date:22-24 March 2016
Abstract: Iterative learning control (ILC) has been intensely researched for over 30 years to improve the performance of repetitive processes. Most ILC algorithms use a known, but potentially inaccurate model to compute the next iteration’s control signal. The majority of publications on the topic of ILC considers linear-time-invariant or linear-parameter-varying systems, although many applications require nonlinear models to represent the system’s dynamics sufficiently. An example for such an application is a robotic manipulator executing the same task repeatedly. This paper adapts a general optimization-based ILC approach for arbitrary nonlinear systems to be used for manipulators with n degrees-of-freedom in a closed-loop configuration. The developed approach is validated both in simulation and experimentally for a 6 degrees-of-freedom robotic manipulator.
Publication status: accepted
KU Leuven publication type: IMa
Appears in Collections:Production Engineering, Machine Design and Automation (PMA) Section
Mechanical Engineering - miscellaneous

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