Fuzzy learning controller design of 2-DOF flight attitude simulator

被引：0

作者：

Ren L.-W. ^{[1
]}

Ban X.-J. ^{[1
]}

Wu F. ^{[2
]}

Huang X.-L. ^{[1
]}

机构：

[1] Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin

[2] Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh

来源：

Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2019年 / 23卷 / 11期

关键词：

Aircraft control; Attitude stabilization; Policy iteration algorithm; Polynomial T-S fuzzy system; Reinforcement learning;

D O I：

10.15938/j.emc.2019.11.016

中图分类号：

学科分类号：

摘要：

Aiming at the attitude stabilization problem of two-degrees-of-freedom flight attitude simulator, an attitude stabilization controller was designed based on the policy iteration algorithm in the reinforcement learning.The policyiteration learning algorithm and the polynomial T-S fuzzy systems were combined together, conducting parameters' adjustment of the controller, and achievingthe optimization of the attitude stability control performance of the two-degrees-of-freedom flight attitude simulator.By approximating the policy function of the actor and the value function of the critic with the polynomial T-S fuzzy models, the actor-critic structure based on the polynomial T-S fuzzy models was established. Through the policy iteration process, the optimal parameters of the controller were learned to minimize the value function.The simulation results show that the policy iteration algorithm based on polynomial T-S fuzzy models is effective in controlling aircraft attitude stabilization. © 2019, Harbin University of Science and Technology Publication. All right reserved.

引用

页码：127 / 134

页数：7

共 21 条

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