Optimization-based iterative learning control for robotic manipulators

摘要

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.