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Performance-Driven Cascade Controller Tuning With Bayesian Optimization
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DOI：10.1109/TIE.2021.3050356.png)
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Abstract
En 中文
In this article, 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.
Keywords:
Tuning
Optimization
Bayes methods
Shafts
Synchronous motors
Induction motors
Permanent magnet motors
Autotuning
Bayesian optimization (BO)
Gaussian process (GP)
PID tuning
Journal
IF:
7.2
Papers:
1.8W
Citations:
9.8W
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Cited Papers
A data-driven approach to robust control of multivariable systems by convex optimization.png)
AUTOMATICA
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AUTOMATICAIF5.9
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