Constrained Bayesian Optimization with Particle Swarms for Safe Adaptive Controller Tuning
| Autor: | Angela P. Schoellig, Andreas Krause, Rikky R. P. R. Duivenvoorden, Nicolas Carion, Felix Berkenkamp |
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| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Adaptive control Discretization Heuristic Heuristic (computer science) Computer science Bayesian optimization Process (computing) 02 engineering and technology Field (computer science) 020901 industrial engineering & automation Control and Systems Engineering Control theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing |
| Zdroj: | IFAC-PapersOnLine. 50:11800-11807 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2017.08.1991 |
| Popis: | 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 L 1 adaptive controller on a quadrotor vehicle and show that we can reliably and automatically tune parameters in experiments. |
| Databáze: | OpenAIRE |
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