Design of a neural network based self-tuning controller for an overhead crane

In the process industry, the use of overhead crane systems for the transportation of material is very common. These are non-linear systems that present undesirable oscillations during the motion, especially at arrival. This paper presents a self-tuning controller based on neural networks for the anti-swing control problem of the crane. The scheme of the controller is based on using neural networks as self-tuners for the parameters of a state feedback controller. The aim of this approach is to take advantage of the ability to learn of the neural networks and to use them in place of an identifier in the conventional self-tuner scheme. One of the main advantages of this method is that the training of the networks is done on-line using a backpropagation algorithm. The algorithm was implemented and tested by means of different simulations carried out with the crane.