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An RBFNN-Based Prescribed Performance Controller for Spacecraft Proximity Operations with Collision Avoidance.

Xianghua Xie1,2, Weidong Chen1, Chengkai Xia2

  • 1State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

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PubMed
Summary
This summary is machine-generated.

A new adaptive robust controller enhances spacecraft control for on-orbit assembly by using Prescribed Performance Control (PPC) and Radial Basis Function Neural Networks (RBFNN). This ensures precise towing of flexible structures while avoiding collisions.

Keywords:
adaptive neural controlmotion controlpredefined boundaryspacecraft

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Area of Science:

  • Robotics and Control Systems
  • Aerospace Engineering
  • Artificial Intelligence

Background:

  • On-orbit assembly (OOA) missions require spacecraft to tow large, flexible structures, facing challenges from unmodeled dynamics and collision risks.
  • Precise control is difficult due to complex coupled dynamics between spacecraft and payloads, and proximity operations demand strict collision avoidance.

Purpose of the Study:

  • To develop a novel adaptive robust controller for spacecraft orbital control during the towing of large, flexible structures in OOA scenarios.
  • To integrate Prescribed Performance Control (PPC) for guaranteed error bounds and collision avoidance with Radial Basis Function Neural Networks (RBFNN) for online compensation of unknown dynamics.

Main Methods:

  • Implementation of a Prescribed Performance Control (PPC) framework to enforce time-varying error boundaries, ensuring intrinsic collision avoidance.
  • Utilization of a Radial Basis Function Neural Network (RBFNN) for online approximation of the combined spacecraft-truss system's unknown nonlinear dynamics.
  • Validation through numerical simulations comparing the proposed controller against a conventional PID controller and hardware experiments on a satellite simulator.

Main Results:

  • The proposed controller demonstrated superior tracking accuracy compared to PID control in simulations, strictly adhering to prescribed error constraints.
  • Hardware experiments confirmed the controller's effectiveness, achieving high-precision trajectory tracking with errors converging to approximately 5 mm.
  • Tracking errors consistently remained within predefined safety boundaries, validating the controller's robustness and safety.

Conclusions:

  • The developed adaptive robust controller offers a reliable and safe solution for complex proximity operations in on-orbit construction.
  • The integration of PPC and RBFNN effectively manages uncertainties from flexible payloads and external disturbances without requiring precise dynamic models.
  • This approach significantly enhances control precision and safety for spacecraft towing large structures in space assembly missions.