Constrained Bayesian optimization with Particle Swarms for Safe Adaptive Controller Tuning
Abstract
Tuning controller parameters is a recurring and time-consuming problem in control. This is especially true in the field of adaptive control, where good performance is typically only achieved after significant tuning. Recently, it has been shown that constrained Bayesian optimization is a promising approach to automate the tuning process without risking system failures during the optimization process. However, this approach is computationally too expensive for tuning more than a couple of parameters. In this paper, we provide a heuristic in order to efficiently perform constrained Bayesian optimization in high-dimensional parameter spaces by using an adaptive discretization based on particle swarms. We apply the method to the tuning problem of an L1 adaptive controller on a quadrotor vehicle and show that we can reliably and automatically tune parameters in experiments. Show more
Publication status
publishedExternal links
Book title
20th IFAC World Congress. ProceedingsJournal / series
IFAC-PapersOnLineVolume
Pages / Article No.
Publisher
ElsevierEvent
Subject
Adaptive Control; Constrained Bayesian Optimization; Safety; Gaussian Process; Particle Swarm Optimization; Policy Search; Reinforcement learningOrganisational unit
03908 - Krause, Andreas / Krause, Andreas
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