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Performance-Driven Cascade Controller Tuning with Bayesian Optimization

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Document pages: 9 pages

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

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