Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation

Area of Science:

• Spacecraft dynamics and control
• Robotics and autonomous systems
• Artificial intelligence in aerospace engineering

Background:

• Spacecraft rendezvous and docking require precise control of coupled position and attitude dynamics.
• Existing control strategies often struggle with system uncertainties and input saturation.
• Adaptive neural networks offer potential for handling complex dynamics and uncertainties.

Purpose of the Study:

• To develop a robust adaptive neural networks control strategy for spacecraft rendezvous and docking.
• To address challenges posed by coupled position-attitude dynamics and input saturation.
• To ensure system stability and performance under uncertain conditions.

Main Methods:

• Utilizing backstepping technique for relative attitude and position controller design.
• Employing radial basis function neural networks (RBFNNs) to approximate dynamics uncertainties.
• Implementing a novel switching controller combining adaptive neural networks and a robust controller.
• Incorporating an auxiliary signal and command filter for input saturation compensation and derivative approximation.

Main Results:

• The proposed control strategy effectively manages coupled position and attitude dynamics.
• The novel switching controller ensures system stability within and outside the neural network's active region.
• Input saturation is successfully compensated using an anti-windup technique.
• Lyapunov theory proves the globally uniformly ultimately bounded stability of relative states.

Conclusions:

• The developed robust adaptive neural networks control strategy is effective for spacecraft rendezvous and docking.
• The approach successfully handles dynamics uncertainties and input saturation, ensuring system stability.
• Simulation results validate the performance and robustness of the proposed control scheme.