Predefined-Time Hierarchical Coordinated Neural Control for Hypersonic Reentry Vehicle

被引：15

作者：

Xu, Bin ^{[1
]}

Shou, Yingxin ^{[1
]}

Shi, Zhongke ^{[1
]}

Yan, Tian ^{[2
]}

机构：

[1] Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China

[2] Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2023年 / 34卷 / 11期

基金：

中国国家自然科学基金; 美国国家科学基金会;

关键词：

Heart rate variability; Aerodynamics; Uncertainty; Couplings; Control systems; Artificial neural networks; Stability criteria; Channel coupling; composite neural learning; hypersonic reentry vehicle (HRV); predefined-time convergency; SLIDING MODE CONTROL; VARIABLE-STRUCTURE CONTROL; NONLINEAR-SYSTEMS; ATTITUDE TRACKING; SPACECRAFT;

D O I：

10.1109/TNNLS.2022.3151198

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper investigates the predefined-time hierarchical coordinated adaptive control on the hypersonic reentry vehicle in presence of low actuator efficiency. In order to compensate for the deficiency of rudder deflection in advantage of channel coupling, the hierarchical design is proposed for coordination of the elevator deflection and aileron deflection. Under the control scheme, the equivalent control law and switching control law are constructed with the predefined-time technology. For the dynamics uncertainty approximation, the composite learning using the tracking error and the prediction error is constructed by designing the serial-parallel estimation model. The closed-loop system stability is analyzed via the Lyapunov approach and the tracking errors are guaranteed to be uniformly ultimately bounded in a predefined time. The tracking performance and the learning accuracy of the proposed algorithm are verified via simulation tests.

引用

页码：8456 / 8466

页数：11

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