Data-Driven Model Discrimination of Switched Nonlinear Systems With Temporal Logic Inference
This article addresses the problem of data-driven model discrimination for unknown switched systems with unknown linear temporal logic (LTL) specifications, representing tasks, that govern their mode sequences, where only sampled data of the unknown dynamics and tasks are available. To tackle this p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of Control Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10271526/ |
| _version_ | 1797384345229459456 |
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| author | Zeyuan Jin Nasim Baharisangari Zhe Xu Sze Zheng Yong |
| author_facet | Zeyuan Jin Nasim Baharisangari Zhe Xu Sze Zheng Yong |
| author_sort | Zeyuan Jin |
| collection | DOAJ |
| description | This article addresses the problem of data-driven model discrimination for unknown switched systems with unknown linear temporal logic (LTL) specifications, representing tasks, that govern their mode sequences, where only sampled data of the unknown dynamics and tasks are available. To tackle this problem, we propose data-driven methods to over-approximate the unknown dynamics and to infer the unknown specifications such that both set-membership models of the unknown dynamics and LTL formulas are guaranteed to include the ground truth model and specification/task. Moreover, we present an optimization-based algorithm for analyzing the distinguishability of a set of learned/inferred model-task pairs as well as a model discrimination algorithm for ruling out model-task pairs from this set that are inconsistent with new observations at run time. Further, we present an approach for reducing the size of inferred specifications to increase the computational efficiency of the model discrimination algorithms. |
| first_indexed | 2024-03-08T21:35:11Z |
| format | Article |
| id | doaj.art-639475d296214bf2a7000821b6890457 |
| institution | Directory Open Access Journal |
| issn | 2694-085X |
| language | English |
| last_indexed | 2024-03-08T21:35:11Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Control Systems |
| spelling | doaj.art-639475d296214bf2a7000821b68904572023-12-21T00:02:04ZengIEEEIEEE Open Journal of Control Systems2694-085X2023-01-01241042410.1109/OJCSYS.2023.332206910271526Data-Driven Model Discrimination of Switched Nonlinear Systems With Temporal Logic InferenceZeyuan Jin0https://orcid.org/0000-0003-1434-4886Nasim Baharisangari1https://orcid.org/0000-0002-3984-8733Zhe Xu2https://orcid.org/0000-0002-0440-0912Sze Zheng Yong3https://orcid.org/0000-0002-2104-3128School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, USASchool for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, USASchool for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, USADepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USAThis article addresses the problem of data-driven model discrimination for unknown switched systems with unknown linear temporal logic (LTL) specifications, representing tasks, that govern their mode sequences, where only sampled data of the unknown dynamics and tasks are available. To tackle this problem, we propose data-driven methods to over-approximate the unknown dynamics and to infer the unknown specifications such that both set-membership models of the unknown dynamics and LTL formulas are guaranteed to include the ground truth model and specification/task. Moreover, we present an optimization-based algorithm for analyzing the distinguishability of a set of learned/inferred model-task pairs as well as a model discrimination algorithm for ruling out model-task pairs from this set that are inconsistent with new observations at run time. Further, we present an approach for reducing the size of inferred specifications to increase the computational efficiency of the model discrimination algorithms.https://ieeexplore.ieee.org/document/10271526/Fault detection and isolationformal verification/synthesislearning for controlmodel validationnonlinear systems identification |
| spellingShingle | Zeyuan Jin Nasim Baharisangari Zhe Xu Sze Zheng Yong Data-Driven Model Discrimination of Switched Nonlinear Systems With Temporal Logic Inference IEEE Open Journal of Control Systems Fault detection and isolation formal verification/synthesis learning for control model validation nonlinear systems identification |
| title | Data-Driven Model Discrimination of Switched Nonlinear Systems With Temporal Logic Inference |
| title_full | Data-Driven Model Discrimination of Switched Nonlinear Systems With Temporal Logic Inference |
| title_fullStr | Data-Driven Model Discrimination of Switched Nonlinear Systems With Temporal Logic Inference |
| title_full_unstemmed | Data-Driven Model Discrimination of Switched Nonlinear Systems With Temporal Logic Inference |
| title_short | Data-Driven Model Discrimination of Switched Nonlinear Systems With Temporal Logic Inference |
| title_sort | data driven model discrimination of switched nonlinear systems with temporal logic inference |
| topic | Fault detection and isolation formal verification/synthesis learning for control model validation nonlinear systems identification |
| url | https://ieeexplore.ieee.org/document/10271526/ |
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