Closing the Loop Inside Neural Networks: Causality-Guided Layer Adaptation for Fault Recovery Control
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915687733133312 |
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| author | Taheri, Mahdi Chung, Soon-Jo Hadaegh, Fred Y. |
| author_facet | Taheri, Mahdi Chung, Soon-Jo Hadaegh, Fred Y. |
| contents | This paper studies the problem of real-time fault recovery control for nonlinear control-affine systems subject to actuator loss of effectiveness faults and external disturbances. We derive a two-stage framework that combines causal inference with selective online adaptation to achieve an effective learning-based recovery control method. In the offline phase, we develop a causal layer attribution technique based on the average causal effect (ACE) to evaluate the relative importance of each layer in a pretrained deep neural network (DNN) controller compensating for faults. This methodology identifies a subset of high-impact layers responsible for robust fault compensation. In the online phase, we deploy a Lyapunov-based gradient update to adapt only the ACE-selected layer to circumvent the need for full-network or last-layer only updates. The proposed adaptive controller guarantees uniform ultimate boundedness (UUB) with exponential convergence of the closed-loop system in the presence of actuator faults and external disturbances. Compared to conventional adaptive DNN controllers with full-network adaptation, our methodology has a reduced computational overhead. To demonstrate the effectiveness of our proposed methodology, a case study is provided on a 3-axis attitude control system of a spacecraft with four reaction wheels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16837 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Closing the Loop Inside Neural Networks: Causality-Guided Layer Adaptation for Fault Recovery Control Taheri, Mahdi Chung, Soon-Jo Hadaegh, Fred Y. Systems and Control This paper studies the problem of real-time fault recovery control for nonlinear control-affine systems subject to actuator loss of effectiveness faults and external disturbances. We derive a two-stage framework that combines causal inference with selective online adaptation to achieve an effective learning-based recovery control method. In the offline phase, we develop a causal layer attribution technique based on the average causal effect (ACE) to evaluate the relative importance of each layer in a pretrained deep neural network (DNN) controller compensating for faults. This methodology identifies a subset of high-impact layers responsible for robust fault compensation. In the online phase, we deploy a Lyapunov-based gradient update to adapt only the ACE-selected layer to circumvent the need for full-network or last-layer only updates. The proposed adaptive controller guarantees uniform ultimate boundedness (UUB) with exponential convergence of the closed-loop system in the presence of actuator faults and external disturbances. Compared to conventional adaptive DNN controllers with full-network adaptation, our methodology has a reduced computational overhead. To demonstrate the effectiveness of our proposed methodology, a case study is provided on a 3-axis attitude control system of a spacecraft with four reaction wheels. |
| title | Closing the Loop Inside Neural Networks: Causality-Guided Layer Adaptation for Fault Recovery Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.16837 |