An Online Adaptive Policy Iteration-Based Reinforcement Learning for a Class of a Nonlinear 3D Overhead Crane

Nezar M. Alyazidi, Abdalrahman M. Hassanine, Magdi S. Mahmoud*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

We consider an online policy iteration-based reinforcement learning for a class of a nonlinear three-dimensional overhead crane with bounded uncertainties. Under the assumption that the rope length is fixed with small swing angles, a linearized model is derived. The system has four states; two actuated states: position x and y, and two un-actuated states, which are the rope angles θx and θy. The adaptive reinforcement learning controller is designed to handle the effects of measurement noises and outliers. We propose a model-free; hence it does not require precise knowledge of the system dynamics. When the state information is not available, a Kalman filter estimator is equipped with a dynamical saturation function to attenuate the effects of measurement noises and to remove outliers. A simulation study is established to illustrate the influence and robustness of the developed controller, and it can enhance the tracking trajectory under different scenarios to test the scheme.

Original languageEnglish
Article number127810
JournalApplied Mathematics and Computation
Volume447
DOIs
StatePublished - 15 Jun 2023

Bibliographical note

Publisher Copyright:
© 2022

Keywords

  • Nnonlinear three-dimensional overhead crane
  • Online policy iteration
  • Ounded uncertainties
  • Reinforcement learning

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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