Safe Risk-Averse Bayesian Optimization for Controller Tuning

Abstract

Controller tuning and parameter optimization are crucial in system design to improve both the controller and underlying system performance. Bayesian optimization has been established as an efficient model-free method for controller tuning and adaptation. Standard methods, however, are not enough for high-precision systems to be robust with respect to unknown input-dependent noise and stable under safety constraints. In this work, we present a novel data-driven approach, RAGoOSe, for safe controller tuning in the presence of heteroscedastic noise, combining safe learning with risk-averse Bayesian optimization. We demonstrate the method for synthetic benchmark and compare its performance to established BO-based tuning methods. We further evaluate RaGoose performance on a real precisionmotion system utilized in semiconductor industry applications and compare it to the built-in auto-tuning routine.

Linear Quadratic Tracking Control of Car-in-the-Loop Test Bench using Model learnt via Bayesian Optimization

“…Bayesian optimization is being continuously developed, for an overview to recent advances of the algorithm the readers can refer to [19]. Due to the learning efficiency and noise toleration, it has great potential for industrial implementations, for example in process systems [20,21], positioning system [22] and robotics [23].…”