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Related Experiment Video

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Neuromorphic robust framework for integrated estimation and control in dynamical systems using spiking neural

Reza Ahmadvand1, Sarah Safura Sharif1, Yaser Mike Banad2

  • 1School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.

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|December 30, 2025
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Summary
This summary is machine-generated.

This study introduces a novel, learning-free spiking neural network (SNN) framework for integrated estimation and control in robotic systems. The SNN-LQR-EMSIF offers computational efficiency and robustness, outperforming traditional methods in dynamic system challenges.

Keywords:
Kalman filterLinear quadratic GaussianNeuromorphic computingSatellite rendezvous maneuverSliding innovation filterSpiking neural network

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Area of Science:

  • Robotics and Dynamical Systems
  • Neuromorphic Computing
  • Control Theory

Background:

  • Integrated estimation and control in robotics face challenges due to state uncertainties and noise.
  • Existing frameworks must balance computational efficiency with accuracy and robustness.
  • Spiking Neural Networks (SNNs) offer potential for efficient processing in dynamic systems.

Purpose of the Study:

  • To present a learning-free SNN framework for integrated estimation and control.
  • To leverage the robustness of the Extended Modified Sliding Innovation Filter (EMSIF) with SNN computational efficiency.
  • To evaluate the proposed SNN-LQR-EMSIF against traditional methods in dynamic system applications.

Main Methods:

  • A recurrent network of Leaky Integrate-and-Fire (LIF) neurons was designed to emulate a Linear Quadratic Regulator (LQR).
  • The framework integrates SNNs with the Extended Modified Sliding Innovation Filter (EMSIF) for robust estimation and control.
  • Weight matrices were tailored to the system model, eliminating the need for network training.

Main Results:

  • The SNN-LQR-EMSIF demonstrated comparable performance to non-spiking LQR-EMSIF and Linear Quadratic Gaussian (LQG) methods.
  • Evaluations in a workbench problem and a satellite rendezvous maneuver (Clohessy-Wiltshire model) confirmed its efficacy.
  • The framework achieved a favorable balance of computational efficiency, robustness, and accuracy.

Conclusions:

  • The proposed SNN-LQR-EMSIF framework is a promising approach for integrated estimation and control in dynamic systems.
  • Its learning-free nature and reliance on SNNs offer significant computational advantages.
  • This method addresses key challenges in robotic systems requiring reliable and efficient control strategies.