Weak Control for Human-in-the-loop Systems
Provides a stability-guaranteed control framework for systems involving multiple human decision makers with private preferences.
Proposed a control framework for human-in-the-loop systems where human decision makers choose from a set of admissible actions, ensuring stability regardless of human choices. A learning algorithm updates controller parameters to reduce minimal costs, demonstrated in a numerical experiment.
In this letter, we propose a control framework for human-in-the-loop systems, in which many human decision makers are involved in the feedback loop composed of a plant and a controller. The novelty of the framework is that the decision makers are weakly controlled; in other words, they receive a set of admissible control actions from the controller and choose one of them in accordance with their private preferences. For example, the decision makers can decide their actions to minimize their own costs or by simply relying on their experience and intuition. A class of controllers which output set-valued signals is proposed, and it is shown that the overall control system is stable independently of the decisions made by the humans. Finally, a learning algorithm is applied to the controller that updates the controller parameters to reduce the achievable minimal costs for the decision makers. Effective use of the algorithm is demonstrated in a numerical experiment.