Active Fault-Tolerant Control for Quadrotor UAV against Sensor Fault Diagnosed by the Auto Sequential Random Forest
Active disturbance rejection control (ADRC) is a model-independent method widely used in passive fault-tolerant control of the quadrotor unmanned aerial vehicle. While ADRC’s effectiveness in actuator fault treatment has been proven, its tolerance to sensor faults requires improvements. In this pape...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/9/9/518 |
| _version_ | 1797492450880651264 |
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| author | Shaojie Ai Jia Song Guobiao Cai Kai Zhao |
| author_facet | Shaojie Ai Jia Song Guobiao Cai Kai Zhao |
| author_sort | Shaojie Ai |
| collection | DOAJ |
| description | Active disturbance rejection control (ADRC) is a model-independent method widely used in passive fault-tolerant control of the quadrotor unmanned aerial vehicle. While ADRC’s effectiveness in actuator fault treatment has been proven, its tolerance to sensor faults requires improvements. In this paper, an ADRC-based active fault-tolerant control (AFTC) scheme is proposed to control the flying attitude against sensor fault for reliability enhancement. Specifically, a semi-model-dependent state tracker is raised to reduce the influence of slow tracking, and accentuate the sensor fault even in varying maneuvers. Derived from the random forest, an enhanced method named auto sequential random forest is designed and applied to isolate and identify faults in real time. Once the tolerance compensation is generated with the fault information, a high-performance AFTC is achieved. The simulation results show that the proposed method can effectively follow the residual when a sensor fault and a change of maneuver occur concurrently. Precise fault information is obtained within 0.04 s, even for small faults on the noise level. The diagnosis accuracy is greater than 86.05% (100% when small faults are excluded), and the identification precision exceeds 97.25%. The short settling time (0.176 s when the small fault is excluded) and modest steady-state error validate the advanced and robust tolerance performance of the proposed AFTC method. |
| first_indexed | 2024-03-10T01:03:52Z |
| format | Article |
| id | doaj.art-c6990afc40934bbf9e4609e059e25b95 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-10T01:03:52Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
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| series | Aerospace |
| spelling | doaj.art-c6990afc40934bbf9e4609e059e25b952023-11-23T14:31:15ZengMDPI AGAerospace2226-43102022-09-019951810.3390/aerospace9090518Active Fault-Tolerant Control for Quadrotor UAV against Sensor Fault Diagnosed by the Auto Sequential Random ForestShaojie Ai0Jia Song1Guobiao Cai2Kai Zhao3School of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Astronautics, Beihang University, Beijing 100191, ChinaActive disturbance rejection control (ADRC) is a model-independent method widely used in passive fault-tolerant control of the quadrotor unmanned aerial vehicle. While ADRC’s effectiveness in actuator fault treatment has been proven, its tolerance to sensor faults requires improvements. In this paper, an ADRC-based active fault-tolerant control (AFTC) scheme is proposed to control the flying attitude against sensor fault for reliability enhancement. Specifically, a semi-model-dependent state tracker is raised to reduce the influence of slow tracking, and accentuate the sensor fault even in varying maneuvers. Derived from the random forest, an enhanced method named auto sequential random forest is designed and applied to isolate and identify faults in real time. Once the tolerance compensation is generated with the fault information, a high-performance AFTC is achieved. The simulation results show that the proposed method can effectively follow the residual when a sensor fault and a change of maneuver occur concurrently. Precise fault information is obtained within 0.04 s, even for small faults on the noise level. The diagnosis accuracy is greater than 86.05% (100% when small faults are excluded), and the identification precision exceeds 97.25%. The short settling time (0.176 s when the small fault is excluded) and modest steady-state error validate the advanced and robust tolerance performance of the proposed AFTC method.https://www.mdpi.com/2226-4310/9/9/518active fault-tolerant controlactive disturbance rejection controlquadrotor unmanned aerial vehiclesensor faultfault diagnosisrandom forest |
| spellingShingle | Shaojie Ai Jia Song Guobiao Cai Kai Zhao Active Fault-Tolerant Control for Quadrotor UAV against Sensor Fault Diagnosed by the Auto Sequential Random Forest Aerospace active fault-tolerant control active disturbance rejection control quadrotor unmanned aerial vehicle sensor fault fault diagnosis random forest |
| title | Active Fault-Tolerant Control for Quadrotor UAV against Sensor Fault Diagnosed by the Auto Sequential Random Forest |
| title_full | Active Fault-Tolerant Control for Quadrotor UAV against Sensor Fault Diagnosed by the Auto Sequential Random Forest |
| title_fullStr | Active Fault-Tolerant Control for Quadrotor UAV against Sensor Fault Diagnosed by the Auto Sequential Random Forest |
| title_full_unstemmed | Active Fault-Tolerant Control for Quadrotor UAV against Sensor Fault Diagnosed by the Auto Sequential Random Forest |
| title_short | Active Fault-Tolerant Control for Quadrotor UAV against Sensor Fault Diagnosed by the Auto Sequential Random Forest |
| title_sort | active fault tolerant control for quadrotor uav against sensor fault diagnosed by the auto sequential random forest |
| topic | active fault-tolerant control active disturbance rejection control quadrotor unmanned aerial vehicle sensor fault fault diagnosis random forest |
| url | https://www.mdpi.com/2226-4310/9/9/518 |
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