A Simplified Finite-Time Fuzzy Neural Controller With Prescribed Performance Applied to Waverider Aircraft

被引：126

作者：

Bu, Xiangwei ^{[1
]}

Qi, Qiang ^{[1
]}

Jiang, Baoxu ^{[1
]}

机构：

[1] Air Force Engn Univ, Missile Def Coll, Xian 710051, Peoples R China

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2022年 / 30卷 / 07期

关键词：

Convergence; Transient analysis; Steady-state; Aircraft; Robustness; Atmospheric modeling; Computational modeling; Finite-time prescribed performance controller; fuzzy-neural approximation; performance functions; small overshoots; waverider aircraft; SYSTEMS;

D O I：

10.1109/TFUZZ.2021.3089031

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article addresses a finite-time prescribed performance controller within the concise fuzzy-neural framework with application to a waverider aircraft. First, new finite-time performance functions are developed to construct a constraint funnel, which accomplishes that tracking errors converge to their steady-state values in a given time (i.e., finite-time convergence), being expected to guarantee tracking errors with small overshoots. Then, the equivalent transformation approach is introduced to unify unknown dynamics such that the control complexity is reduced. Moreover, to further reduce computational costs, a single-learning-parameter-based regulation scheme is developed for fuzzy-neural approximation. Finally, the proposed method is applied to a waverider aircraft to test its effectiveness and superiority.

引用

页码：2529 / 2537

页数：9

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