Fuzzy Neural Network Q-Learning Method for Model Disturbance Change: A Deployable Antenna Panel Application. (14th December 2019)

• Record Type: Journal Article
• Authors: Liu, Zhiyong; Bao, Hong; Xue, Song; Du, Jingli
• Other Names: Concilio Antonio Academic Editor.
• Is Part Of: International journal of aerospace engineering. Volume 2019(2019)
• Publication Date: 2019-12-14
• Subjects: Aerospace engineering -- Periodicals; 629.105
• DOI: 10.1155/2019/6745045
• Languages: English
• ISSNs: 1687-5966

Abstract

Abstract : This paper addresses the disturbance change control problem with an active deformation adjustment mechanism on a 5-meter deployable antenna panel. A fuzzy neural network Q-learning control (FNNQL) strategy is proposed in this paper for the disturbance change to improve the accuracy of the antenna panel. In the proposed method, the error of the model disturbance is reduced by introducing the fuzzy radial basis function (RBF) neural network into Q-learning, and the parameters of the fuzzy RBF neural network were optimized and adjusted by a Q-learning method. This allows the FNNQL controller to have a strong adaptability to deal with the disturbance change. Finally, the proposed method has been adopted in the middle plate of a 5-meter deployable antenna panel, and it was found that the method could successfully adapt the model disturbance change in the antenna panel. Results of the simulation also show that the whole control system meets the required accuracy requirements.