Abstract
This work investigates optimal stabilization with guaranteed worst-case performance of stochastic discrete event systems by supervisory control. We formulate the problem on probabilistic weighted automata. The system is driven to a specified set of target states after a finite number of transitions, thus stabilized. The cost of stabilization is concerned with the accumulative weight of transitions reaching target states. Our goal is to optimize the expected cost of reaching target states, while ensuring that the worst-case individual cost is bounded by a given threshold. Then we transform the supervisory control problem to a two-player stochastic game between the supervisor and the environment, which properly encodes the worst-case requirement. Finally an algorithm is presented to synthesize the optimal supervisor by leveraging results from Markov Decision Processes, which turns out to provably solve the original problem.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
