Efficient tuning for motion control in diverse systems: a Bayesian framework

Feed-forward control is widely used in motion control systems that involve repetitive tasks, leading to substantial performance improvements. This paper presents a model-free feedforward optimization framework centred around Bayesian Optimization (BO). Bypassing the need for exhaustive system modelling, the method directly optimizes the Iterative Learning Control (ILC) degrees of freedom based on a user-defined parametrization of the feed-forward controller. Experimental results on a motion control application show significant improvements with respect to more classical ILC. A notable advantage emerges when dealing with an industrially relevant case with multiple similar plants; the optimizer is shown to adeptly adjust the feed-forward control to be compliant with the response of the measured system.