Performance-Driven Cascade Controller Tuning with Bayesian Optimization

TL;DR

This paper introduces a Bayesian optimization-based autotuning method for cascade control systems, improving tuning efficiency, robustness, and tracking performance through joint parameter optimization.

Contribution

It presents a novel automated tuning approach for cascade controllers using Bayesian optimization, guaranteeing convergence to the global optimum.

Findings

Method is data-efficient and converges to optimal parameters.

Enhanced robustness against disturbances compared to classical tuning.

Improved tracking performance demonstrated on real systems.

Abstract

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.