An application of a neural self-tuning controller to an overhead crane

A neural network-based self-tuning controller is presented. The scheme of t he controller is based on using a multilayer perceptron, or a set of them, as a self-tuner for a controller. The method proposed has the advantage tha t it is not necessary to use a combined structure of identification and dec ision, common in a standard self-tuning controller. The paper explains the algorithm for a general case, and then a specific application on a nonlinea r plant is presented. The plant is an overhead crane which involves an inte resting control problem related to the oscillations of the load mass. The m ethod pro posed is tested by simulation in different conditions. A comparis on was made with a conventional controller to evaluate the efficiency of th e algorithm.