Title :
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
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007464
