On the Design and Implementation of a Neuromorphic Self-Tuning Controller

  • Authors:

  • L. Acosta;J. A. Méndez;S. Torres;L. Moreno;G. N. Marichal

  • Affiliations:

  • Dept. of Applied Physics, University of La Laguna, 38271 La Laguna, Tenerife, Spain, e-mail: jamp@cyc.dfis.ull.es;Dept. of Applied Physics, University of La Laguna, 38271 La Laguna, Tenerife, Spain, e-mail: jamp@cyc.dfis.ull.es;Dept. of Applied Physics, University of La Laguna, 38271 La Laguna, Tenerife, Spain, e-mail: jamp@cyc.dfis.ull.es;Dept. of Applied Physics, University of La Laguna, 38271 La Laguna, Tenerife, Spain, e-mail: jamp@cyc.dfis.ull.es;Dept. of Applied Physics, University of La Laguna, 38271 La Laguna, Tenerife, Spain, e-mail: jamp@cyc.dfis.ull.es

  • Venue:
  • Neural Processing Letters
  • Year:
  • 1999

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Abstract

This paper deals with the design and implementation of a neural network-based self-tuning controller. The structure of the controller is based on using a neural network, or a set of them, as a self-tuner for a controller. The intention 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. The work is divided into two main parts. The first one is dedicated to the design of the self-controller. And the second is an application of the algorithm on a nonlinear system: an overhead crane. Some simulations were carried out to verify the efficiency of the self-tuner and then a real-time implementation on a scale prototype was performed.