Anti interference and fault tolerant control of UAVs integrating residual based diagnosis disturbance estimation with counter drone strategies | Scientific Reports
AI Analysis
The article introduces an integrated control framework for UAVs that combines fault detection, disturbance estimation, and counter-UAV strategies to enhance resilience against faults and adversarial conditions. This approach demonstrates improved performance in maintaining UAV stability and executing evasive maneuvers.
Key Takeaways
- Integration of fault detection, disturbance estimation, and counter-UAV strategies.
- Use of residual-based fault detection and isolation.
- Adaptive extended state observer for disturbance estimation.
- Nonlinear UAV dynamic model analyzed under Lyapunov stability theory.
- 100% success rate in counter-drone evasive maneuvers.
Why It Matters
This integrated framework significantly enhances UAV resilience in adversarial environments, which is crucial for both civilian and military applications. By improving UAV stability and maneuverability, this approach could lead to more reliable and effective deployment of UAVs in complex scenarios, potentially influencing future UAV control systems and counter-UAS strategies.
Anti interference and fault tolerant control of UAVs integrating residual based diagnosis disturbance estimation with counter drone strategies | Scientific Reports
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Subjects
- [Mathematics and computing]
- [Engineering]
Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in complex, uncertain, and adversarial environments, yet they remain vulnerable to actuator faults, environmental disturbances, and deliberate interferences such as jamming or spoofing. Conventional control approaches typically address these challenges independently, focusing on either fault-tolerant control (FTC), disturbance rejection, or counter-UAV defense. This paper presents an integrated anti-interference and fault-tolerant control framework that unifies three complementary modules: (i) residual-based fault detection and isolation, (ii) adaptive extended state observer (AESO)-based disturbance estimation and compensation, and (iii) counter-UAV evasive maneuver strategies. The framework is formulated using a nonlinear UAV dynamic model and rigorously analyzed under Lyapunov stability theory. Simulation results demonstrate significant performance improvements compared with baseline controllers. The proposed method reduced position and attitude deviations to below 0.05 m and 0.03 deg, respectively, under simultaneous actuator faults and wind disturbances. Fault and disturbance estimation errors remained below 0.05 and 0.03 units, respectively, ensuring timely control reconfiguration. Furthermore, the UAV achieved a 100% success rate in counter-drone evasive maneuvers while maintaining trajectory stability. These results confirm that the integrated design provides high resilience, rapid recovery, and reliable performance in fault-disturbed and adversarial conditions. By bridging FTC, adaptive disturbance rejection, and counter-UAV defense, the proposed framework advances the state of the art in resilient UAV control for civilian and defense applications.
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
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