Efficient safe learning for controller tuning with experimental validation
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Date
2025-03-01
Publication Type
Journal Article
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yes
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Abstract
Optimization-based controller tuning is challenging because it requires formulating optimization problems explicitly as functions of controller parameters. Safe learning algorithms overcome the challenge by creating surrogate models from measured data. To ensure safety, such data-driven algorithms often rely on exhaustive grid search, which is computationally inefficient. In this paper, we propose a novel approach to safe learning by formulating a series of optimization problems instead of a grid search. We also develop a method for initializing the optimization problems to guarantee feasibility while using numerical solvers. The performance of the new method is first validated in a simulated precision motion system, demonstrating improved computational efficiency, and illustrating the role of exploiting numerical solvers to reach the desired precision. Experimental validation on an industrial-grade precision motion system confirms that the proposed algorithm achieves 30% better tracking at sub-micrometer precision as a state-of-the-art safe learning algorithm, improves the default auto-tuning solution, and reduces the computational cost seven times compared to learning algorithms based on exhaustive search.
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Publication status
published
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Journal / series
Volume
143
Pages / Article No.
109894
Publisher
Elsevier
Event
Edition / version
Methods
Software
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Date collected
Date created
Subject
Active learning; Controller tuning; Bayesian optimization; Safe learning; Gaussian process regression
Organisational unit
03751 - Lygeros, John / Lygeros, John
Notes
Funding
180545 - NCCR Automation (phase I) (SNF)
787845 - Optimal control at large (EC)
787845 - Optimal control at large (EC)
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