| 1. |
- Zhang, Huimin, et al.
(författare)
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A learning-based synthesis approach to the supremal nonblocking supervisor of discrete-event systems
- 2018
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE. - 0018-9286 .- 1558-2523. ; 63:10, s. 3345-3360
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Tidskriftsartikel (refereegranskat)abstract
- The paper presents a novel approach to synthesize supremal nonblocking supervisors of discrete-event systems (DES), when the automaton models of specifications are not available. Extending the L* learning algorithm, an S* algorithm is developed to infer a tentatively correct supervisor. If the tentatively correct supervisor is nonblocking, it is indeed the supremal nonblocking supervisor with respect to the plant and specifications. Otherwise, the blocking automaton is regarded as a new plant, and the specification is the nonblocking property. Then, the supremal nonblocking supervisor with respect to the new problem is computed using supervisory control theory of DES. Two simplification rules are introduced to the S* algorithm to decrease the computational cost. Finally, the S* algorithm is implemented based on the LearnLib framework, and experiments are performed to verify the proposed approach.
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| 2. |
- Zhang, Huimin, et al.
(författare)
-
Integration of Learning-Based Testing and Supervisory Control for Requirements Conformance of Black-Box Reactive Systems
- 2018
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Ingår i: IEEE Transactions on Automation Science and Engineering. - : IEEE. - 1545-5955 .- 1558-3783. ; 15:1, s. 2-15
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Tidskriftsartikel (refereegranskat)abstract
- A fundamental requirement of the supervisory control theory (SCT) of discrete-event systems is a finite automaton model of the plant. The requirement does not hold for black-box systems whose source code and logical model are not accessible. To apply SCT to black-box systems, we integrate automaton learning technology with SCT and apply the new method to improve the requirements conformance of software reuse. If the reused software component does not satisfy a requirement, the method adds a supervisor component to prevent the black-box system from reaching ''faulty sections.'' The method employs learning-based testing (LBT) to verify whether the reused software meets all requirements in the new context. LBT generates a large number of test cases and iteratively constructs an automaton model of the system under test. If the system fails the test, the learned model is applied as the plant model for control synthesis using SCT. Then, the supervisor is implemented as an executable program to monitor and control the system to follow the requirement. Finally, the integrated system, including the supervisory program and the reused component, is tested by LBT to assure the satisfiability of the requirement. This paper makes two contributions. First, we innovatively integrate LBT and SCT for the control synthesis of black-box reactive systems. Second, software component reuse is still possible even if it does not satisfy user requirements at the outset.
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