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
收藏
页码:1702 / 1711
页数:10
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