A self-tuning neuromorphic controller:: application to the crane problem

This paper is concerned with the design and application of a self-tuning controller, aided by means of neural networks (NN). The structure of the controller is based on the use of neural networks as an implicit self-tuner for the controller. The aim of this approach is to take advantage of the learning properties of the neural networks to increase the performance of the self-tuning. The application of this technique is performed on an overhead crane. The control objective is to suppress undesirable oscillations during operation of the crane. First, some simulations were carried out, as well as a comparison with a standard self-tuning method, that demonstrate the advantages of this method. After this, a real-time implementation on a scale prototype of a crane was done to verify the applicability of the method. (C) 1998 Elsevier Science Ltd. All rights reserved.