Transformation Model of Thrust-vectoring Using RBF Neural Network

摘要：

　　In this paper,a transformation model between the thrust-vectoring vane deflections and the resultant thrust deviation angles is established for the thrust-vectoring with three vane construction based on the radial basis function(RBF)neural network.The RBF neural network is trained using the experiment transformation data from NASA research memorandum via the generalized growing and pruning algorithm(GGAP).The established RBF neural network model can eliminate the inaccuracy of existing estimation model and avoids the modeling difficulties using the experiment data.To test the correctness of the transformation model using RBF neural network,it is compared with the existing estimation model.Through the simulation results,one can obtain that the RBF neural network transformation model established in this paper has a global and accurate description for the transformation relationship between the thrust-vectoring vane deflections and the resultant thrust deviation angles.Moreover,it can show the characteristics of the thrust-vectoring more precisely.

关键词： Thrust-Vectoring control (TVC) RBF neural network Transformation model Three-vane construction thrust-vectoring

作者: YONG Kenan YE Hui CHEN Mou WU Qingxian

作者单位: Nanjing University of Aeronautics and Astronautics,College of Automation Engineering,Nanjing 210016

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 4997-5002

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）