DDEKF学习快速非线性自适应逆控制

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290) Pub Date : 2002-08-07 DOI:10.1109/IJCNN.2002.1007464

G. Plett, H. Bottrich

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引用次数: 10

摘要

自适应逆控制(AIC)采用三种自适应滤波器:对象模型、控制器和干扰消除器。如果工厂是线性的，则方法可以快速有效地训练这些过滤器;然而，已知的非线性AIC方法学习非常缓慢。本文采用动态解耦扩展卡尔曼滤波(DDEKF)对基于实时循环学习的标准非线性AIC学习方法进行了改进。训练变得更快了。

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DDEKF learning for fast nonlinear adaptive inverse control

Adaptive inverse control (AIC) uses three adaptive filters: plant model, controller and disturbance canceler. Methods are known for quick and efficient training of these filters if the plant is linear; however, known methods for nonlinear AIC learn very slowly. This paper modifies the standard nonlinear AIC learning methods (based on real-time recurrent learning) using the dynamic-decoupled-extended Kalman-filter (DDEKF). The training becomes significantly faster.

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Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)

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