Fault-tolerant Q-learning for discrete-time linear systems with actuator and sensor faults using input-output measured data
This study presents a novel output feedback Q-learning algorithm specifically designed for fault-tolerant control in real-time applications, circumventing the necessity for explicit system models or detailed actuator and sensor fault information. A significant benefit of this algorithm is its capabi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Franklin Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186325000490 |
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| author | Mohammadrasoul Kankashvar Sajad Rafiee Hossein Bolandi |
| author_facet | Mohammadrasoul Kankashvar Sajad Rafiee Hossein Bolandi |
| author_sort | Mohammadrasoul Kankashvar |
| collection | DOAJ |
| description | This study presents a novel output feedback Q-learning algorithm specifically designed for fault-tolerant control in real-time applications, circumventing the necessity for explicit system models or detailed actuator and sensor fault information. A significant benefit of this algorithm is its capability to simultaneously achieve optimality and stabilize systems with both actuator and sensor faults. Unlike traditional methods, it learns online using input-output data from the faulty system, bypassing the need for full-state measurements. We develop a unique expression of the Fault-Tolerant Q-function (FTQF) in the input-output format and derive a model-free optimal output feedback fault-tolerant control (FTC) policy. Furthermore, the algorithm's real-time implementation process is detailed, showing its adaptability in acquiring optimal output feedback FTC policies without prior knowledge of system dynamics or faults. The proposed method remains unaffected by excitation noise bias, even without a discount factor, and guarantees closed-loop stability and convergence to optimal solutions. Validation through numerical simulations on an F-16 autopilot aircraft underscores its effectiveness. |
| format | Article |
| id | doaj-art-d40e344ef4e142bbb911ac843d6bcc81 |
| institution | Kabale University |
| issn | 2773-1863 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj-art-d40e344ef4e142bbb911ac843d6bcc812025-08-20T03:30:32ZengElsevierFranklin Open2773-18632025-06-011110025910.1016/j.fraope.2025.100259Fault-tolerant Q-learning for discrete-time linear systems with actuator and sensor faults using input-output measured dataMohammadrasoul Kankashvar0Sajad Rafiee1Hossein Bolandi2Corresponding author.; Department of Control Systems Engineering, Iran University of Science and Technology, Tehran 1684613114, IranDepartment of Control Systems Engineering, Iran University of Science and Technology, Tehran 1684613114, IranDepartment of Control Systems Engineering, Iran University of Science and Technology, Tehran 1684613114, IranThis study presents a novel output feedback Q-learning algorithm specifically designed for fault-tolerant control in real-time applications, circumventing the necessity for explicit system models or detailed actuator and sensor fault information. A significant benefit of this algorithm is its capability to simultaneously achieve optimality and stabilize systems with both actuator and sensor faults. Unlike traditional methods, it learns online using input-output data from the faulty system, bypassing the need for full-state measurements. We develop a unique expression of the Fault-Tolerant Q-function (FTQF) in the input-output format and derive a model-free optimal output feedback fault-tolerant control (FTC) policy. Furthermore, the algorithm's real-time implementation process is detailed, showing its adaptability in acquiring optimal output feedback FTC policies without prior knowledge of system dynamics or faults. The proposed method remains unaffected by excitation noise bias, even without a discount factor, and guarantees closed-loop stability and convergence to optimal solutions. Validation through numerical simulations on an F-16 autopilot aircraft underscores its effectiveness.http://www.sciencedirect.com/science/article/pii/S2773186325000490Reinforcement learningFault-tolerant controlActuator faultsSensor faultsOutput feedbackQ-learning |
| spellingShingle | Mohammadrasoul Kankashvar Sajad Rafiee Hossein Bolandi Fault-tolerant Q-learning for discrete-time linear systems with actuator and sensor faults using input-output measured data Franklin Open Reinforcement learning Fault-tolerant control Actuator faults Sensor faults Output feedback Q-learning |
| title | Fault-tolerant Q-learning for discrete-time linear systems with actuator and sensor faults using input-output measured data |
| title_full | Fault-tolerant Q-learning for discrete-time linear systems with actuator and sensor faults using input-output measured data |
| title_fullStr | Fault-tolerant Q-learning for discrete-time linear systems with actuator and sensor faults using input-output measured data |
| title_full_unstemmed | Fault-tolerant Q-learning for discrete-time linear systems with actuator and sensor faults using input-output measured data |
| title_short | Fault-tolerant Q-learning for discrete-time linear systems with actuator and sensor faults using input-output measured data |
| title_sort | fault tolerant q learning for discrete time linear systems with actuator and sensor faults using input output measured data |
| topic | Reinforcement learning Fault-tolerant control Actuator faults Sensor faults Output feedback Q-learning |
| url | http://www.sciencedirect.com/science/article/pii/S2773186325000490 |
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