Human-in-the-loop controller tuning using Preferential Bayesian Optimization

The development of human-centric platforms that are able to combine computational resources and advanced analytics with human judgment and qualitative processing ability is a key driver of the Industry 5.0 movement. In this setting, humans are not only active in the loop but also play a key role in the decision-making process. In this work, we propose the use of Preferential Bayesian Optimization (PBO) for human-in-the-loop controller tuning. PBO relies on pairwise comparisons and preference feedback (A is better than B) to search for the optimal trade-of between different performance criteria from the user’s perspective. The advantages of PBO are demonstrated in a simulated Proportional Integral (PI) controller tuning example with real user feedback under a reduced number of experiments. The results show that PBO leads to a greater emphasis on closed-loop responses closer to the user’s desired behavior when compared with multi-objective alternatives, while being straightforward to implement from the user’s perspective.