Personalization of Control Systems by Policy Update With Improving User Trust

In this letter, we address the design of personalized control systems, which pursue an individual and private objective defined for each user. To this end, a problem of policy update is formulated where an individual objective function is estimated and the corresponding optimal control law is updated. The novelty of the problem setting is in the presence of a system-user and the policy update driven by his/her rating. The system-user rates on the control system to be updated and the rating is used for estimating his/her objective function. It is assumed that the rating depends not only on his/her private objective but also on his/her trust on the control system. Then, we address the problem of the policy update improving the rating while not impairing the trust. Algorithms of the policy update, which is essentially the objective function estimation, are developed and their convergence analysis is presented. Finally, through a numerical experiment, the effectiveness of the algorithms is shown.