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Adaptive Bayesian Optimization for High-Precision Motion Systems
We further evaluate the algorithm's performance on a real precision-motion system utilized in semiconductor industry applications by modifying the payload and reference stepsize and comparing it to an interpolated constrained optimization-based baseline approach.
Efficient safe learning for controller tuning with experimental validation
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.
Safe and Efficient Model-free Adaptive Control via Bayesian Optimization
Thus, our approach builds on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization.
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization
This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization.
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