scholarly journals Adversarially Robust Bayesian Optimization for Efficient Auto‐Tuning of Generic Control Structures under Uncertainty

AIChE Journal ◽  
2022 ◽  
Author(s):  
Joel A. Paulson ◽  
Georgios Makrygiorgos ◽  
Ali Mesbah
Keyword(s):  
Bayesian Optimization ◽  
Control Structures ◽  
Auto Tuning

scholarly journals Adversarially Robust Bayesian Optimization for Efficient Auto-Tuning of Generic Control Structures under Uncertainty

2021 ◽  
Author(s):  
Joel Paulson ◽  
Georgios Makrygiorgos ◽  
Ali Mesbah
Keyword(s):  
Process Model ◽  
Closed Loop ◽  
Bayesian Optimization ◽  
Small Scale ◽  
Loop Control ◽  
Control Structures ◽  
Tuning Parameters ◽  
Highly Nonlinear ◽  
Auto Tuning ◽  
Time Invariant

The performance of advanced controllers depends on the selection of several tuning parameters that can affect the closed-loop control performance and constraint satisfaction in highly nonlinear and nonconvex ways. There has been a significant interest in auto-tuning of complex control structures using Bayesian optimization (BO). However, an open challenge is how to deal with uncertainties in the closed-loop system that cannot be attributed to a lumped, small-scale noise term. This paper develops an adversarially robust BO (ARBO) method that is suited to auto-tuning problems with significant time-invariant uncertainties in a plant simulator. ARBO uses a Gaussian process model that jointly describes the effect of the tuning parameters and uncertainties on the closed-loop performance. ARBO uses an alternating confidence-bound procedure to simultaneously select the next candidate tuning and uncertainty realizations, implying only one expensive closed-loop simulation is needed at each iteration. The advantages of ARBO are demonstrated on two case studies.

scholarly journals Bayesian Optimization for auto-tuning GPU kernels

2021 ◽  
Author(s):  
Floris-Jan Willemsen ◽  
Rob van Nieuwpoort ◽  
Ben van Werkhoven
Keyword(s):  
Bayesian Optimization ◽  
Auto Tuning
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