Adaptive Dynamic Inversion via Time-Scale Separation

被引：27

作者：

Hovakimyan, Naira ^{[1
]}

Lavretsky, Eugene ^{[2
]}

Cao, Chengyu ^{[3
]}

机构：

[1] Univ Illinois, Dept Mech Sci & Engn, Urbana, IL 61801 USA

[2] Boeing Co, Phantom Works, Huntington Beach, CA 92649 USA

[3] Univ Connecticut, Storrs, CT 06269 USA

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2008年 / 19卷 / 10期

关键词：

Adaptive control; nonaffine systems; time-scale separation; ultimate boundedness;

D O I：

10.1109/TNN.2008.2001221

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 presents a full state feedback adaptive dynamic inversion method for uncertain systems that depend nonlinearly upon the control input. Using a specialized set of basis functions that respect the monotonic property of the system nonlinearities with respect to control input, a state predictor is defined for derivation of the adaptive laws. The adaptive dynamic inversion controller is defined as a solution of a fast dynamical equation, which achieves time-scale separation between the state predictor and the controller dynamics. Lyapunov-based adaptive laws ensure that the predictor tracks the state of the nonlinear system with bounded errors. As a result, the system state tracks the desired reference model with bounded errors. Benefits of the proposed design method are demonstrated using Van der Pol dynamics with nonlinear control input.

引用

页码：1702 / 1711

页数：10

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