  We propose a performance-based autotuning method for cascade control systems,
where the parameters of a linear axis drive motion controller from two control
loops are tuned jointly. Using Bayesian optimization as all parameters are
tuned simultaneously, the method is guaranteed to converge asymptotically to
the global optimum of the cost. The data-efficiency and performance of the
method are studied numerically for several training configurations and compared
numerically to those achieved with classical tuning methods and to the
exhaustive evaluation of the cost. On the real system, the tracking performance
and robustness against disturbances are compared experimentally to nominal
tuning. The numerical study and the experimental data both demonstrate that the
proposed automated tuning method is efficient in terms of required tuning
iterations, robust to disturbances, and results in improved tracking.
