A neuro-controller for synchronization of two motion axes

The coordinated and synchronized control of the motion of multiple axes is a challenging problem in motion control fields. In most multiaxis applications, controllers are usually designed for each of the motion axes, which results in a collection of decoupled single input and single output systems. For coordinated motion, however, decoupling sometimes causes damage to the overall performance objective. Therefore, a better way to control multiaxis systems is to introduce intelligent control actions in the controller so that the coordination objective of the desired motion is maintained. A method for achieving the synchronization of two motion axes using a neural network is described. We introduce a new cost function for better synchronization performance. Also, we derive a learning law to adjust the weights of the neural network, based on the gradient algorithm. The derived learning law guarantees good synchronization performance of two motion axes. Simulation and experimental results demonstrate the usefulness of the proposed scheme to synchronize the motion of multiple axes.