Lateral Control Law Design for Helicopter Using Radial Basis Function Neural Network

摘要：

As fixed-parameter control can not satisfy control requirements when helicopter is aviating in large scale flight envelop, this paper proposes a new control law design to adjust parameters on-line. Firstly a parameter-mapping approach is developed to design flight control parameters at certain flight conditions according to the desired system performance. Then parameters obtained at given conditions are used to train Radial Basis Function Neural Network (RBFNN). Thus RBFNN can generalize the given flight conditions information and output appropriate control parameters which will meet control requirements for any current flight condition within the given flight envelop. Simulation results indicate this control law design is feasible and effective.

关键词： T-S model Radial Basis Function Neural Network Parameter mapping Flight control

作者: Jingchao Lu Qiong Ling Jiaming Zhang

作者单位: Department of Automatic Control Northwestern Polytechnical University Xian, ShaanXi, China 710072

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）