Operating mode of UAV fault handling module

Key Technical Challenges

Currently, achieving an efficient and reliable fault handling system faces the following main challenges:

Complexity versus Real-Time Performance: UAV systems contain numerous sensors and actuators, which may experience complex faults involving coupling, concurrency, or gradual changes. Traditional flight control computer redundancy management and fault isolation consume significant computing resources, increasing system load and impacting real-time performance. Simultaneously, completing the closed loop from diagnosis to control within milliseconds demands extremely high algorithm efficiency.

Model Dependence versus Environmental Uncertainty: Many diagnostic methods based on mathematical models (such as Kalman filtering) heavily rely on the accuracy of the model. However, in actual flight, UAVs face uncertain disturbances such as aerodynamic parameter changes, gusts, and icing, as well as unknown nonlinear faults in actuators and sensors caused by mechanical wear and environmental influences (such as icing). These factors significantly complicate accurate modeling and fault isolation.

Onboard Resource Constraints versus Algorithm Deployment: UAV onboard computing power, storage, and power consumption are strictly limited. However, advanced intelligent diagnostic algorithms (such as deep learning models) typically have high computational demands and are difficult to deploy directly. A difficult balance must be struck between algorithm performance, complexity, and real-time performance.

Frontier Technology Breakthrough Directions

1. Intelligent Fusion and Advanced Diagnostics: The core is to improve the accuracy and adaptability of diagnostics. For example, combining radial basis function (RBF) neural networks with nonlinear dynamic inverse (NDI) controllers can adaptively estimate and compensate for dynamic, unknown actuator and sensor composite faults caused by environmental factors such as icing. Furthermore, using nonlinear disturbance observers (NDOs) to "package" and estimate various disturbances can effectively enhance system robustness.

2. Highly Efficient Fault-Tolerant Control Algorithms: The focus is on maximizing resource optimization while ensuring stability. For example, combining dynamic event-triggered control with predefined time stability theory allows the system to communicate and compute only when necessary. Related research shows that compared to traditional time-triggered mechanisms, network resource consumption can be reduced by 69.4%. Additionally, fixed-time/predefined-time control ensures that the system state converges within a preset time independent of the initial state, providing precise performance boundaries.

3. System Architecture Optimization: Aiming to improve reliability from the top-level design. On the one hand, a dedicated multi-source redundant fault-tolerant navigation computing system is designed to separate tasks such as signal processing and redundancy management from the flight control computer, reducing its load and improving overall reliability. On the other hand, a distributed fault diagnosis system is developed to rationally allocate diagnostic tasks to airborne equipment and ground stations, utilizing the stronger computing power of the ground for in-depth analysis.