Supervisory control of a class of discrete event processes
SIAM Journal on Control and Optimization
The Challenge of Building Process-Control Software
IEEE Software
Model-Checking Algorithms for Continuous-Time Markov Chains
IEEE Transactions on Software Engineering
Model checking meets performance evaluation
ACM SIGMETRICS Performance Evaluation Review
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
ICALP '08 Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part II
Interactive Markov chains: and the quest for quantified quality
Interactive Markov chains: and the quest for quantified quality
Towards Supervisory Control of Interactive Markov Chains: Controllability
ACSD '11 Proceedings of the 2011 Eleventh International Conference on Application of Concurrency to System Design
On the markovian randomized strategy of controller for markov decision processes
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Verifying Performance of Supervised Plants
ACSD '12 Proceedings of the 2012 12th International Conference on Application of Concurrency to System Design
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We propose to integrate performance evaluation with supervisory control synthesis to bring higher confidence in the control design. Supervisory control theory deals with automatic synthesis of supervisory controllers that ensure safe behavior of the supervised system, based on the models of the uncontrolled system and the (safety) control requirements. For the purpose of performance evaluation, we turn to stochastic model checking of continuous-time Markov chains, which requires an extension of the model of the uncontrolled system with Markovian delays. We cast our proposal as an extension of a model-based systems engineering framework that relies on supervisor synthesis. We treat the Markovian delays syntactically, exploiting their equivalent interleaving behavior with uniquely-named uncontrollable transitions. In this way, we can employ already available synthesis tools, while preserving the stochastic behavior. To this end, we develop model transformation tools to extract the underlying Markov process from the stochastic discrete-event model of the supervised system. We illustrate the approach by modeling a pipeless plant that employs automated guided vehicles instead of fixed piping in order to ensure greater flexibility of the plant. The control problem that we solve is safe high-level movement coordination of the vehicles, ensured by the supervisory controller. We show how to seamlessly introduce stochastic behavior in the supervised system and we evaluate several performance and reliability aspects of the plant. We implement the framework by interfacing two state-of-the-art tools: Supremica for supervisory controller synthesis and MRMC for Markovian model checking. To this end, we improve previous attempts by providing support for data-based observers, which greatly improve the modeling capabilities of the framework.