A neural network development environment for adaptive inverse control

This paper presents a neurocontroller development environment using the ideas of adaptive inverse control. The goal in this paper is to describe the environment utilized, and show a very simple example. A PC running a commercial simulator communicates in real-time with a mu controller to learn how to control the power supply of a motor using the speed as input. This mu controller runs a feedforward neural network online, but the weights are adapted periodically off-line by the simulator and downloaded once the weights stabilize. The user interacts with the nice user interface of the commercial package to define the topology, to test the adequacy of the control topology and to set the control parameters.