Safe Bayesian Optimisation for Controller Design by Utilising the Parameter Space Approach

As control systems become more and more complex, the optimal tuning of control parameters using Bayesian Optimisation gained an increased interest of research in recent years. Safe Bayesian Optimisation, tries to prevent sampling of unsafe parametrizations and therefore allow parameter tuning in real world experiments. Usually this is achieved by approximating a safe set using probabilistic GPR-predictions. In contrast in this work, analytical knowledge about robustly stable parameter configurations is gained by the parameter space approach and then incorporated within the optimisation as constraint. Simulation results on a linear system with uncertain parameters show a significant performance gain compared to standard approaches. .