Hiroaki GOMI, Mitsuo KAWATO
Neural-Network Control
for a Closed-Loop System
using Feedback-Error-Learning
Abstract:New learning schemes using feedback-error-learning for a neural network model
applied to adaptive nonlinear feedback control are presented here. Feedback-error learning
was proposed as a learning method to acquire a feedforward controller
which uses the output of a feedback controller as the error for training a neural
network model. Using new schemes for nonlinear feedback. control, the actual
responses after learning correspond to the desired responses which are obtained by
an inverse reference model implemented as the conventional feedback controller. In
this respect, these methods are similar to Model Reference Adaptive Control(MRAC) applied to linear or linearized systems. It is shown that learning
impedance control is derived when proposed schemes are used in Cartesian space.
Convergence properties of the neural networks employed in these learning schemes
are provided by an averaged equation and the Liapunov function method. Some
results of simulating these learning schemes by using an inverted pendulum and a
2-link manipulator are also presented.