Ht. Chiu et S. Cetinkunt, TRAINABLE NEURAL-NETWORK FOR MECHANICALLY FLEXIBLE SYSTEMS BASED ON NONLINEAR FILTERING, Journal of guidance, control, and dynamics, 18(3), 1995, pp. 503-507
A trainable neural network controller architecture is investigated for
motion control systems involving significant distributed mechanical f
lexibility. In general, this neural network based controller can be tr
ained on-fine to learn the behavior of another another controller whic
h serves as the teacher implementing algorithmic or nonalgorithmic con
trol law. To address potential of such a scheme in real time, the weig
ht adjustments of the network connection strengths and biases are base
d on a nonlinear filtering adaptation rule, extended Kalman filter, to
reduce training time and achieve fast convergence rate. Computer simu
lations are performed to test the performance of this training algorit
hm.