Title :
An adaptive extended Kalman filter using artificial neural networks
Author :
Stubberud, Stephen C. ; Lobbia, Robert N. ; Owen, Mark
Author_Institution :
Orincon Corp., San Diego, CA, USA
Abstract :
Develops an adaptive state-estimation technique using artificial neural networks, referred to as a neuro-observer. The neuro-observer is an extended Kalman filter structure that has its state-coupling function augmented by an artificial neural network that captures the unmodeled dynamics. The neural network of the neuro-observer trains on-line using an extended Kalman filter training paradigm. Improvement in the system model then provides for a more accurate state estimate in the feedback loop, thus enhancing the control signal so that the system behaves in a closer to optimal fashion
Keywords :
adaptive Kalman filters; feedback; neural nets; observers; adaptive extended Kalman filter; adaptive state-estimation technique; artificial neural networks; control signal; feedback loop; neuro-observer; state-coupling function; Additive noise; Artificial neural networks; Feedback control; Feedback loop; Jacobian matrices; Multi-layer neural network; Neural networks; Noise measurement; Optimal control; State estimation;
Conference_Titel :
Decision and Control, 1995., Proceedings of the 34th IEEE Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-2685-7
DOI :
10.1109/CDC.1995.480611