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.
Original language | English |
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Book series | IFAC-PapersOnLine |
Volume | 52 |
Issue number | 8 |
Pages (from-to) | 194-199 |
Number of pages | 6 |
ISSN | 1474-6670 |
DOIs | |
Publication status | Published - Sept 2019 |
Event | IFAC Symposium on Intelligent Autonomous Vehicles - 10th IAV 2019 - Gdansk, Poland Duration: 3 Jul 2019 → 5 Jul 2019 http://www.konsulting.gda.pl/iav2019/web/ |
Conference
Conference | IFAC Symposium on Intelligent Autonomous Vehicles - 10th IAV 2019 |
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Country/Territory | Poland |
City | Gdansk |
Period | 03/07/2019 → 05/07/2019 |
Internet address |
Keywords
- Reinforcement learning
- Optimal control
- Neural network
- Inner-outer loop control
- inner-outer loop control
- optimal control
- neural network