Runtime Monitoring and Fault Detection for Neural Network-Controlled Systems
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914727374880768 |
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| author | Lan, Jianglin Zhan, Siyuan Patton, Ron Zhao, Xianxian |
| author_facet | Lan, Jianglin Zhan, Siyuan Patton, Ron Zhao, Xianxian |
| contents | There is an emerging trend in applying deep learning methods to control complex nonlinear systems. This paper considers enhancing the runtime safety of nonlinear systems controlled by neural networks in the presence of disturbance and measurement noise. A robustly stable interval observer is designed to generate sound and precise lower and upper bounds for the neural network, nonlinear function, and system state. The obtained interval is utilised to monitor the real-time system safety and detect faults in the system outputs or actuators. An adaptive cruise control vehicular system is simulated to demonstrate effectiveness of the proposed design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16132 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Runtime Monitoring and Fault Detection for Neural Network-Controlled Systems Lan, Jianglin Zhan, Siyuan Patton, Ron Zhao, Xianxian Systems and Control Machine Learning There is an emerging trend in applying deep learning methods to control complex nonlinear systems. This paper considers enhancing the runtime safety of nonlinear systems controlled by neural networks in the presence of disturbance and measurement noise. A robustly stable interval observer is designed to generate sound and precise lower and upper bounds for the neural network, nonlinear function, and system state. The obtained interval is utilised to monitor the real-time system safety and detect faults in the system outputs or actuators. An adaptive cruise control vehicular system is simulated to demonstrate effectiveness of the proposed design. |
| title | Runtime Monitoring and Fault Detection for Neural Network-Controlled Systems |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2403.16132 |