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December 11, 2025RoboticaOpen Access

Experimental iterative learning control of a quadrotor in flight: A derivation of the state-dependent Riccati equation method

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Authors

SNSaeed Rafee NekooAOAnibal Ollero

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Overview

Experimental work shows iterative learning control enhances drone stabilization and trajectory tracking.

Key Points

  • This research aims to enhance quadrotor flight control using iterative learning control and optimal strategies.
  • Utilized iterative learning control for quadrotor flight improvement.
  • Implemented state-dependent Riccati equation to stabilize the quadrotor in initial loops.
  • Employed gradient descent method for error reduction during control training.
  • Conducted tests in simulation and real flight scenarios.
  • Iterative learning control significantly improved trajectory tracking accuracy for the quadrotor.
  • The state-dependent Riccati equation effectively stabilized the quadrotor during initial iterations.
  • Successful implementation of the control method on a flying drone for the first time.

Cite This Study

Nekoo et al. (2025) studied this question.

synapsesocial.com/papers/6940192a2d562116f28f6bc1https://doi.org/10.1017/s0263574725102919
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