Neural Network Modeling-Based Anti-Disturbance Tracking Control for Hypersonic Flight Vehicle Models
作者单位：College of Information EngineeringYangzhou University School of ComputingEngineering and MathematicsUniversity of Western Sydney
会议名称：《第36届中国控制会议》
主办单位：Dalian University of Technology;Systems Engineering Society of China (SESC);Technical Committee on Control Theory (TCCT), Chinese Association of Automation (CAA)
会议日期：2017年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 081105[工学-导航、制导与控制] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by National Natural Science Foundation of China under Grant(61473249,61503329,61603610) a research grant from the Australian Research Council
关 键 词：anti-disturbance control hypersonic flight vehicle tracking control neural network identifier disturbance observer
摘 要：This paper discusses the novel anti-disturbance control algorithm for hypersonic flight vehicle（HFV） models by using neural network（NN） identifier. Different from those existed anti-disturbance results, the unknown exogenous disturbances in HFV models are assumed to be described by the designed NNs with adjustable parameters. Furthermore, the disturbanceobserver-based-control（DOBC） algorithm with adaptive regulation laws is thus presented to estimate the nonlinear *** integrating the estimated value of disturbances with the PI feedback control input, a composite controller based on convex optimization theory is generated to ensure the satisfactory stability and dynamical tacking convergence of HFV models. Finally,a numerical example for HFV models is included to illustrate the effectiveness of the theoretical results.