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

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

Applied Mathematics and Computation, 2023, vol. 447, issue C

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.

Keywords: Online policy iteration; Reinforcement learning; Nnonlinear three-dimensional overhead crane; Ounded uncertainties (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:447:y:2023:i:c:s0096300322008785

DOI: 10.1016/j.amc.2022.127810

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