Article
Version 1
Preserved in Portico This version is not peer-reviewed
Adaptive Control of Quadrotors in Uncertain Environments
Version 1
: Received: 11 March 2024 / Approved: 11 March 2024 / Online: 11 March 2024 (13:57:45 CET)
A peer-reviewed article of this Preprint also exists.
Leitão, D.; Cunha, R.; Lemos, J.M. Adaptive Control of Quadrotors in Uncertain Environments. Eng 2024, 5, 544-561. Leitão, D.; Cunha, R.; Lemos, J.M. Adaptive Control of Quadrotors in Uncertain Environments. Eng 2024, 5, 544-561.
Abstract
The problem addressed in this article consists of the motion control of a quadrotor, comprising model disturbances and uncertainties. In order to tackle model uncertainty, adaptive control based on reinforcement learning is used. The distinctive feature of this article, in comparison with other works on quadrotor control using reinforcement learning is the exploration of the underlying optimal control problem in which a quadratic cost and a linear dynamics allow an algorithm that runs in real time. Instead of identifying a plant model, adaptation is obtained by approximating the performance index given by the Q-function using directional forgetting recursive least squares that rely on a linear regressor build from quadratic functions of input/output data. The adaptive algorithm proposed is tested in simulation in a cascade control structure that drives a quadrotor. Simulations show the improvement in performance that results when the proposed algorithm is turned on.
Keywords
Quadrotor control; adaptive control; reinforcement learning
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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