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  • 1.
    Farooqui, Ashfaq
    et al.
    RISE Research Institutes of Sweden, Safety and Transport, Electrification and Reliability.
    Claase, Tijsse
    Eindhoven University of Technology, Netherlands.
    Fabian, Martin
    Chalmers University of Technology, Sweden.
    On Active Learning for Supervisor Synthesis2024In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 21, p. 78-Article in journal (Refereed)
    Abstract [en]

    Supervisory control theory provides an approach to synthesize supervisors for cyber-physical systems using a model of the uncontrolled plant and its specifications. These supervisors can help guarantee the correctness of the closed-loop controlled system. However, access to plant models is a bottleneck for many industries, as manually developing these models is an error-prone and time-consuming process. An approach to obtaining a supervisor in the absence of plant models would help industrial adoption of supervisory control techniques. This paper presents, an algorithm to learn a controllable supervisor in the absence of plant models. It does so by actively interacting with a simulation of the plant by means of queries. If the obtained supervisor is blocking, existing synthesis techniques are employed to prune the blocking supervisor and obtain the controllable and non-blocking supervisor. Additionally, this paper presents an approach to interface the with a PLC to learn supervisors in a virtual commissioning setting. This approach is demonstrated by learning a supervisor of the well-known example simulated in Xcelgo Experior and controlled using a PLC. interacts with the PLC and learns a controllable supervisor for the simulated system. Note to Practitioners—Ensuring the correctness of automated systems is crucial. Supervisory control theory proposes techniques to help build control solutions that have certain correctness guarantees. These techniques rely on a model of the system. However, such models are typically unavailable and hard to create. Active learning is a promising technique to learn models by interacting with the system to be learned. This paper aims to integrate active learning and supervisory control such that the manual step of creating models is no longer needed, thus, allowing the use of supervisory control techniques in the absence of models. The proposed approach is implemented in a tool and demonstrated using a case study. 

  • 2.
    Hagebring, Fredrik
    et al.
    Chalmers University of Technology, Sweden.
    Farooqui, Ashfaq
    RISE Research Institutes of Sweden, Safety and Transport, Electrification and Reliability.
    Fabian, Martin
    Chalmers University of Technology, Sweden.
    Lennartson, Bengt
    Chalmers University of Technology, Sweden.
    On Optimization of Automation Systems: Integrating Modular Learning and Optimization2022In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 19, no 3, p. 1662-1674Article in journal (Refereed)
    Abstract [en]

    Compositional Optimization (CompOpt) was recently proposed for optimization of discrete-event systems of systems. A modular optimization model allows CompOpt to divide the optimization into separate sub-problems, mitigating the state space explosion problem. This paper presents the Modular Optimization Learner (MOL), a method that interacts with a simulation of a system to automatically learn these modular optimization models. MOL uses a modular learning that takes as input a hypothesis structure of the system and uses the provided structural information to split the acquired learning into a set of modules, and to prune parts of the search space. Experiments show that modular learning reduces the state space by many orders of magnitude compared to a monolithic learning, which enables learning of much larger systems. Furthermore, an integrated greedy search heuristic allows MOL to remove many sub-optimal paths in the individual modules, speeding up the subsequent optimization.

  • 3.
    Raza, Shahid
    et al.
    RISE, Swedish ICT, SICS, Security Lab.
    Seitz, Ludwig
    RISE, Swedish ICT, SICS.
    Sytenkov, Denis
    RISE, Swedish ICT, SICS.
    Selander, Göran
    Ericsson, Sweden.
    S3K: Scalable Security With Symmetric Keys—DTLS Key Establishment for the Internet of Things2016In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 13, no 3, p. 1270-1280Article in journal (Refereed)
    Abstract [en]

    DTLS is becoming the de facto standard for communication security in the Internet of Things (IoT). In order to run the DTLS protocol, one needs to establish keys between the communicating devices. The default method of key establishment requires X.509 certificates and a Public Key Infrastructure, an approach which is often too resource consuming for small IoT devices. DTLS also supports the use of preshared keys and raw public keys. These modes are more lightweight, but they are not scalable to a large number of devices.

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