Supervisory Control under Local Mean Payoff Constraints
Yiding Ji, Xiang Yin, Stéphane Lafortune · 2019
This work investigates quantitative supervisory control with a local mean payoff objective for weighted discrete event systems. Weight flows are generated by the system and a supervisor must be designed to ensure that the mean payoff of weights over a fixed number of transitions never drops below a given threshold while the system is operating. The local mean payoff may be viewed as a stability measure of weight flows. We formulate the supervisory control problem and transform it to a two-player game between the supervisor and the environment. Next, window payoff functions are defined to characterize the objective for the supervisor in the game. Then we analyze the game and develop a method to synthesize game-winning supervisors, which solve the proposed problem.