Cascade Control: Data-Driven Tuning Approach Based on Bayesian Optimization
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Author / Producer
Date
2020-11
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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Data
Abstract
Cascaded controller tuning is a multi-step iterative procedure that needs to be performed routinely upon maintenance and modification of mechanical systems. An automated data-driven method for cascaded controller tuning based on Bayesian optimization is proposed. The method is tested on a linear axis drive, modeled using a combination of first principles model and system identification. A custom cost function based on performance indicators derived from system data at different candidate configurations of controller parameters is modeled by a Gaussian process. It is further optimized by minimization of an acquisition function which serves as a sampling criterion to determine the subsequent candidate configuration for experimental trial and improvement of the cost model iteratively, until a minimum according to a termination criterion is found. This results in a data-efficient procedure that can be easily adapted to varying loads or mechanical modifications of the system. The method is further compared to several classical methods for auto-tuning, and demonstrates higher performance according to the defined data-driven performance indicators. The influence of the training data on a cost prior on the number of iterations required to reach optimum is studied, demonstrating the efficiency of the Bayesian optimization tuning method.
Permanent link
Publication status
published
External links
Book title
21st IFAC World Congress
Journal / series
Volume
53 (2)
Pages / Article No.
382 - 387
Publisher
Elsevier
Event
1st Virtual IFAC World Congress (IFAC-V 2020)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
PID tuning; Auto-tuning; Gaussian process; Bayesian optimization
Organisational unit
08814 - Smith, Roy (Tit.-Prof.) (ehemalig) / Smith, Roy (Tit.-Prof.) (former)
03751 - Lygeros, John / Lygeros, John
Notes
Due to the Coronavirus (COVID-19) the 21st IFAC World Congress 2020 became the 1st Virtual IFAC World Congress (IFAC-V 2020).