Neural control of a nonlinear system with inherent time delays

Abstract

We have used a small robot arm to study the use of neural networka as adaptive controller and neural emulators. Our objectives were to investigate nonlinear systems that are accompanied by large time delays. Such systems can be difficult to control, since delays in feedback loops often give rise to instabilities. We have trained neural network emulators to simulate the operation of this system using a database of dynamic stimulus-response. Conventional methods of indirect learning -back-propagating errors through the emulator -to train an inverse kinematic feedforward controller do not work for such systems. Instead, it is necessary to provide the controller with the capability to anticipate future target trajectories. We present an example of such a controller, its function and performance in our prototypical system.