A Controller Design for Approaching Disabled Satellites Based on Discrete Sample Points.

Area of Science:

• Aerospace Engineering
• Control Systems
• Robotics

Background:

• Accurate control is crucial for disabled satellite servicing to prevent mission failure and further space debris generation.
• Existing control methods may lack the precision required for complex proximity operations.

Purpose of the Study:

• To propose and evaluate a novel controller driven by discrete sample data points for enhanced accuracy in satellite servicing.
• To compare the performance of the proposed discrete point controller against a neural network controller.

Main Methods:

• Input vectors are mapped to a state space, alongside pre-generated sample points and control commands.
• Data processing techniques including dichotomy, table look-up, and random selection are employed for efficiency.
• Control commands are computed using the iteratively reweighted least-squares algorithm, assuming input-output similarity.

Main Results:

• The discrete point controller demonstrated higher precision compared to the neural network controller in simulations.
• The implemented data processing methods accelerated the controller's operational speed.

Conclusions:

• The discrete point controller offers a more precise and efficient solution for autonomous satellite servicing and debris mitigation.
• This approach enhances mission success rates and contributes to a cleaner space environment.