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Updated: Nov 16, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Fast real-time SDRE controllers using neural networks.

Rômulo Fernandes da Costa1, Osamu Saotome2, Elvira Rafikova3

  • 1Graduate Program in Electronic and Computer Engineering - Electronic Devices and Systems, Electronic Engineering Division, Aeronautics Institute of Technology (ITA), 50 Praça Marechal Eduardo Gomes, São José dos Campos, SP, 12228-900, Brazil.

ISA Transactions
|February 22, 2021
PubMed
Summary
This summary is machine-generated.

Artificial neural networks (ANNs) replicate fast state-dependent Riccati equation (SDRE) control for satellite attitude dynamics. Neural controllers offer reduced complexity and high-speed execution, performing equivalently to traditional SDRE methods.

Keywords:
Deep learningNeural controlSDRE controlSatellite attitude controlStacked denoising autoencoders

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • State-dependent Riccati equation (SDRE) control is a powerful technique for nonlinear systems.
  • Implementing SDRE controllers can be computationally intensive, limiting their application in high-speed scenarios.
  • Artificial neural networks (ANNs) offer potential for approximating complex control functions.

Purpose of the Study:

  • To implement fast state-dependent Riccati equation (SDRE) control algorithms using artificial neural networks (ANNs).
  • To evaluate the efficacy of ANNs in replicating SDRE controllers for satellite attitude dynamics.
  • To demonstrate the potential for reduced computational complexity and higher execution rates with neural network controllers.

Main Methods:

  • Training shallow and deep artificial neural networks (ANNs) to emulate a pre-designed SDRE controller.
  • Utilizing a satellite attitude dynamics simulator (SADS) for developing and testing the SDRE controller.
  • Validating a trained neural network controller through practical experimentation on the SADS.

Main Results:

  • Trained ANNs successfully replicated the functionality of the original SDRE controller.
  • Neural network controllers exhibited significantly reduced computational complexity.
  • Experimental validation confirmed that the neural controller performed equivalently to the original SDRE controller with small training errors.

Conclusions:

  • Fast SDRE control algorithms can be effectively implemented using ANNs.
  • ANN-based controllers offer a computationally efficient alternative to traditional SDRE methods for satellite attitude control.
  • The proposed technique demonstrates practical viability for high-rate control applications.