文章基本信息

标题：Optimal Tracking Control Based on Integral Reinforcement Learning for An Underactuated Drone
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作者：Shaobao Li ; Petar Durdevic ; Zhenyu Yang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：8
页码：55-60
DOI：10.1016/j.ifacol.2019.08.048
语种：English
出版社：Elsevier
摘要：AbstractA 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.
关键词：KeywordsReinforcement learningoptimal controlneural networkinner-outer loop control