Adaptive learning-based optimal tracking control system design and analysis of a disturbed nonlinear hypersonic vehicle model  

作　　者：AN Kai WANG ZhenGuo HUANG Wei 

机构地区：[1]Hypersonic Technology Laboratory,National University of Defense Technology,Changsha,410073,China

出　　处：《Science China(Technological Sciences)》2024年第6期1893-1906,共14页中国科学（技术科学英文版）

基　　金：supported by the Natural Science Foundation of Hunan Province(Grant No.2021JJ10045);the National Natural Science Foundation of China(Grant No.11972368);the National Key R&D Program of China(Grant No.2019YFA0405300)。

摘　　要：We propose an adaptive learning-based optimal control scheme for height-velocity control models considering model uncertainties and external disturbances of hypersonic winged-cone vehicles.The longitudinal nonlinear model is first established and transformed into the control-oriented error equations,and the control scheme is organized by a steady-compensation combination.To overcome and eliminate the impact of model uncertainties and external disturbances,an adaptive radial basis function neural network(RBFNN)is designed by a q-gradient approach.Taking the height-velocity error system with estimated uncertainties into account,the adaptive learning-based optimal tracking control(ALOTC)scheme is proposed by combining the critic-only adaptive dynamic programming(ADP)framework and parameter optimization of system settling time.Furthermore,a novel weight update law is proposed to satisfy the online iteration requirements,and the algorithm convergence and closedloop stability are discussed by the Lyapunov theory.Finally,four simulation cases are provided to prove the effectiveness,accuracy,and robustness of the proposed scheme for the hypersonic longitudinal control system.

关 键 词：optimal tracking control adaptive dynamic programming RBFNN hypersonic vehicle 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] V249[自动化与计算机技术—控制科学与工程] V448[航空宇航科学与技术—飞行器设计]