DDEKF learning for fast nonlinear adaptive inverse control

Author

Plett, Gregory L. ; Böttrich, Hans

Author_Institution

Dept. of Electr. & Comput. Eng., Colorado Univ., Colorado Springs, CO, USA

Volume

3

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

2092

Lastpage

2097

Abstract

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

Keywords

Kalman filters; MIMO systems; adaptive control; feedforward neural nets; identification; learning (artificial intelligence); nonlinear control systems; real-time systems; recurrent neural nets; MIMO system; adaptive filters; adaptive inverse control; disturbance canceling; dynamic-decoupled-extended Kalman-filter; feedforward neural network; identification; nonlinear control; real-time learning; recurrent neural networks; Adaptive control; Adaptive filters; Adaptive systems; Automatic control; Delay lines; Neural networks; Neurons; Nonlinear dynamical systems; Programmable control; Springs;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007464

Filename

1007464