Model reference adaptive control with recurrent networks trained by the dynamic DEKF algorithm

Two fundamental extensions of the dynamic backpropagation (DBP) gradient descent procedure which generally result in faster convergence times and higher quality solutions are presented. The decoupled extended Kalman filter training algorithm (DEKF) for feedforward layered networks is extended to the training of neural controllers in a dynamic indirect adaptive control scheme; the resulting algorithm is called dynamic DEKF (or DDEKF). The DDEKF neural controller training algorithm is extended to include control network architectures with explicit internal feedback connections. It is demonstrated that the DDEKF algorithm has computational complexity and requirements that are similar to those of DBP for control networks with a large number of recurrent connections. The use of these extensions for a model reference adaptive control (MRAC) problem in which the example dynamical system is highly nonlinear and does not possess a unique inverse is presented