Abstract
A drone is desirable to perform various flying missions with different loads while always guaranteeing optimal flying performance. In this paper, an integral reinforcement learning algorithm is developed for a drone such that it can learn optimal control policy online. The drone is described by an underactuated nonlinear model and the inner-outer loop control strategy is applied for the navigation control. In the outer loop an optimal controller is designed to minimize a cost function with input saturation, and a policy iteration based integral reinforcement learning (IRL) algorithm is proposed. Critic-actor neural networks (NNs) are further applied for online implementation of the IRL algorithm. In the inner loop a quaternion based feedback attitude controller is designed to guarantee system stability. A simulation study is finally provided to demonstrate the effectiveness of the proposed IRL algorithm.
Originalsprog | Engelsk |
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Bogserie | IFAC-PapersOnLine |
Vol/bind | 52 |
Udgave nummer | 8 |
Sider (fra-til) | 194-199 |
Antal sider | 6 |
ISSN | 1474-6670 |
DOI | |
Status | Udgivet - sep. 2019 |
Begivenhed | IFAC Symposium on Intelligent Autonomous Vehicles - 10th IAV 2019 - Gdansk, Polen Varighed: 3 jul. 2019 → 5 jul. 2019 http://www.konsulting.gda.pl/iav2019/web/ |
Konference
Konference | IFAC Symposium on Intelligent Autonomous Vehicles - 10th IAV 2019 |
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Land/Område | Polen |
By | Gdansk |
Periode | 03/07/2019 → 05/07/2019 |
Internetadresse |