Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations

Area of Science:

• Aerospace Engineering
• Control Systems Theory
• Artificial Intelligence

Background:

• Spacecraft attitude control is critical for mission success.
• Existing methods struggle with model uncertainties, external disturbances, and component failures.
• Resource limitations pose additional challenges for spacecraft control systems.

Purpose of the Study:

• To develop robust attitude tracking control methods for spacecraft.
• To address challenges including model uncertainties, external disturbances, and subsystem faults/failures.
• To design controllers that operate effectively under limited resources and input saturations.

Main Methods:

• A novel intelligent control algorithm combining radial basis function neural networks (RBFNNs) for approximation and tunable parameter-based variable structure (TPVS) control techniques.
• Development of adaptive control strategies by carefully selecting adaptation parameters.
• Utilization of Lyapunov theory to prove system stability and convergence.

Main Results:

• The proposed methods ensure finite-time convergence of the closed-loop system states.
• Demonstrated fault-tolerance capability against actuator faults/failures and input saturations.
• Validation of the control strategies through five illustrative numerical examples.

Conclusions:

• The developed intelligent control methods offer robust and fault-tolerant attitude tracking for spacecraft.
• The combination of RBFNNs and TPVS control effectively handles complex operational challenges.
• The proposed approach guarantees reliable spacecraft attitude control under various adverse conditions.