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Publications (5 of 5) Show all publications
Farooqui, A., Claase, T. & Fabian, M. (2024). On Active Learning for Supervisor Synthesis. IEEE Transactions on Automation Science and Engineering, 21, 78
Open this publication in new window or tab >>On Active Learning for Supervisor Synthesis
2024 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 21, p. 78-Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2024
Keywords
active learning, Atmospheric modeling, automata learning, Behavioral sciences, Computational modeling, Discrete-event systems, Industries, Learning automata, Software, Supervisory control, supervisory control theory, Automata theory, Automation, Behavioral research, Embedded systems, Learning systems, Supervisory personnel, Virtual reality, Automaton learning, Behavioral science, Computational modelling, Discrete events systems, Discrete event simulation
National Category
Control Engineering
Identifiers
urn:nbn:se:ri:diva-61432 (URN)10.1109/TASE.2022.3216759 (DOI)2-s2.0-85141508588 (Scopus ID)
Note

This work wassupported in part by the Wallenberg AI, Autonomous Systems and SoftwareProgram (WASP) by the Knut and Alice Wallenberg Foundation; and in partby the Verification and Validation of Automated Systems’ Safety and Security(VALU3S) Project, from the Electronic Components and Systems for European Leadership (ECSEL) 

Available from: 2022-12-07 Created: 2022-12-07 Last updated: 2024-05-27Bibliographically approved
Maleki, M., Farooqui, A. & Sangchoolie, B. (2023). CarFASE: A Carla-based Tool for Evaluating the Effects of Faults and Attacks on Autonomous Driving Stacks. In: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W): . Paper presented at 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) (pp. 92-99). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>CarFASE: A Carla-based Tool for Evaluating the Effects of Faults and Attacks on Autonomous Driving Stacks
2023 (English)In: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 92-99Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents CarFASE, an open-source carla-based fault and attack simulation engine that is used to test and evaluate the behavior of autonomous driving stacks in the presence of faults and attacks. Carla is a highly customizable and adaptable simulator for autonomous driving research. In this paper, we demonstrate the application of CarFASE by running fault injection experiments on OpenPilot, an open-source advanced driver assistance system designed to provide a suite of features such as lane keeping, adaptive cruise control, and forward collision warning to enhance the driving experience. A braking scenario is used to study the behavior of OpenPilot in the presence of brightness and salt&pepper faults. The results demonstrate the usefulness of the tool in evaluating the safety attributes of autonomous driving systems in a safe and controlled environment.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Computer Systems
Identifiers
urn:nbn:se:ri:diva-66359 (URN)10.1109/dsn-w58399.2023.00036 (DOI)
Conference
53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)
Note

This work was supported by VALU3S project, which has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 876852. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Czech Republic, Germany, Ireland, Italy, Portugal, Spain, Sweden, Turkey

Available from: 2023-09-05 Created: 2023-09-05 Last updated: 2023-09-05Bibliographically approved
Farooqui, A. & Sangchoolie, B. (2023). Towards Formal Fault Injection for Safety Assessment of Automated Systems. In: Fifth International Workshop on Formal Methods for Autonomous Systems: . Paper presented at International Workshop on Formal Methods for Autonomous Systems.
Open this publication in new window or tab >>Towards Formal Fault Injection for Safety Assessment of Automated Systems
2023 (English)In: Fifth International Workshop on Formal Methods for Autonomous Systems, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Reasoning about safety, security, and other dependability attributes of autonomous systems is a challenge that needs to be addressed before the adoption of such systems in day-to-day life. Formal methods is a class of methods that mathematically reason about a system’s behavior. Thus, a correctness proof is sufficient to conclude the system’s dependability. However, these methods are usually applied to abstract models of the system, which might not fully represent the actual system. Fault injection, on the other hand, is a testing method to evaluate the dependability of systems. However, the amount of testing required to evaluate the system is rather large and often a problem. This vision paper introduces formal fault injection, a fusion of these two techniques throughout the development lifecycle to enhance the dependability of autonomous systems. We advocate for a more cohesive approach by identifying five areas of mutual support between formal methods and fault injection. By forging stronger ties between the two fields, we pave the way for developing safe and dependable autonomous systems. This paper delves into the integration’s potential and outlines future research avenues, addressing open challenges along the way.

Keywords
Fault injection, formal methods
National Category
Computer Systems
Identifiers
urn:nbn:se:ri:diva-67578 (URN)
Conference
International Workshop on Formal Methods for Autonomous Systems
Note

This work was partly supported by the VALU3S project, which has received funding from the ECSEL Joint Undertaking(JU) under grant agreement No 876852. The JU receives support from the European Union’s Horizon 2020 research andinnovation programme and Austria, Czech Republic, Germany, Ireland, Italy, Portugal, Spain, Sweden, Turkey. This work hasalso been partly financed by the CyReV project, which is funded by the VINNOVA FFI program – the Swedish GovernmentalAgency for Innovation Systems (Diary number: 2019-03071).

Available from: 2023-10-31 Created: 2023-10-31 Last updated: 2023-11-01Bibliographically approved
Damschen, M., Farooqui, A., Häll, R., Landström, P. & Thorsén, A. (2022). Development and onboard assessment of drone for assistance in firefighting resource management and rescue operations.
Open this publication in new window or tab >>Development and onboard assessment of drone for assistance in firefighting resource management and rescue operations
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2022 (English)Report (Other academic)
Abstract [en]

This report provides comprehensive information for deciding whether to pursue the deployment of adrone system for increasing safety on ship. The assessments of technical and legal feasibility as wellas usefulness of a drone system for surveying the open decks of a ro-ro ship are presented. The usecases of fire patrol, fire resource management and search & rescue operations are targeted. Aprototype drone system is detailed that is built on open standards and open-source software for highextensibility and reproducibility. Technical feasibility is assessed positively overall using a purpose-designed drone-control software, in-field tests and a demonstration onboard of DFDS PetuniaSeaways. The needs for further development, analysis and long-term tests are described. The legalfeasibility assessment gives an overview of applicable maritime and airspace regulations within theEU. It concludes that the drone system should be seen complementary to existing fire safety systemsand that operational authorization is best applied for in collaboration with a ship owner. Usefulnessis assessed using responses from maritime experts to an online questionnaire on the targeted usecases. Results are positive with two major challenges identified: achieving a reasonable selling priceand obtaining the ship operators’ and crews’ trust in the system. Finally, a SWOT analysis gives aconcise summary of the performed assessments and can be used as input to the strategic businessplanning for a potential drone system provider.

Publisher
p. 78
Keywords
UAV, Drone, Maritime Safety, Fire Safety, Automation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:ri:diva-73543 (URN)
Projects
LASH FIRE
Funder
EU, Horizon 2020, 814975
Note

Call identifier: H2020-MG-2018-Two-Stages Stages MG-2.2-2018: Marine Accident Response, Subtopic C

Available from: 2024-06-11 Created: 2024-06-11 Last updated: 2024-06-11Bibliographically approved
Hagebring, F., Farooqui, A., Fabian, M. & Lennartson, B. (2022). On Optimization of Automation Systems: Integrating Modular Learning and Optimization. IEEE Transactions on Automation Science and Engineering, 19(3), 1662-1674
Open this publication in new window or tab >>On Optimization of Automation Systems: Integrating Modular Learning and Optimization
2022 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 19, no 3, p. 1662-1674Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2022
Keywords
Automata, Automation, control systems, Learning automata, learning automata., Multiprotocol label switching, Optimization, Software algorithms, Task analysis, Discrete event simulation, Finite automata, Job analysis, Automaton, Learning automaton., Modular learning, Modular optimizations, Optimisations
National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-59065 (URN)10.1109/TASE.2022.3144230 (DOI)2-s2.0-85126294749 (Scopus ID)
Available from: 2022-04-14 Created: 2022-04-14 Last updated: 2023-03-23Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1559-7896

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