Neurointerfaces: principles

A neurointerface is a trainable filter based on neural networks that serves as a coupler between a human operator and a nonlinear system or plant that is to be controlled or directed. The purpose of the coupler is to ease the task of the human controller. The equations of the plant are assumed to be known. If the plant is unstable, it must first be stabilized by feedback. Using the plant equations, off-line automatic learning algorithms are developed for training the weights of the neurointerface. If the plant is subject to disturbance, an adaptive disturbance canceller is used to minimize the effect. The neurointerface can be adapted to be an inverse of the plant, so that when it is cascaded with the plant, the overall plant response closely approximates the human command input.