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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Optimality principles in spacecraft neural guidance and control.

Dario Izzo1, Emmanuel Blazquez1, Robin Ferede2

  • 1Advanced Concepts Team, European Space Research & Technology Centre, Keplerlaan 1, 2200 AG Noordwijk, Netherlands.

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Neural networks are learning optimal control for space missions, enabling real-time onboard decision-making. This approach enhances spacecraft autonomy and robustness for interplanetary transfers and landings.

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Area of Science:

  • Robotics and Autonomous Systems
  • Aerospace Engineering
  • Artificial Intelligence

Background:

  • Space missions require efficient onboard resource management for success.
  • Traditional methods rely on ground-based optimal control plans.
  • Onboard computation of guidance and control is crucial for autonomy.

Purpose of the Study:

  • To review end-to-end neural networks for spacecraft guidance and control.
  • To highlight the learning of optimality principles by neural models.
  • To explore drone racing as a testbed for these AI architectures.

Main Methods:

  • Review of end-to-end neural networks, termed guidance and control networks (G&CNets).
  • Analysis of G&CNets for interplanetary transfers, planetary landings, and close-proximity operations.
  • Utilizing drone racing as a simulated environment for testing G&CNets.

Main Results:

  • Neural networks successfully learn and apply optimality principles for spacecraft control.
  • G&CNets enable real-time onboard computation of optimal actions from sensor data.
  • Drone racing environments validate G&CNets' performance under uncertainty.

Conclusions:

  • End-to-end neural networks offer a path to increased spacecraft autonomy and robustness.
  • G&CNets can adapt to complex dynamics and uncertainties, crucial for space exploration.
  • Drone racing provides a valuable platform for advancing AI in space robotics.