Learning soft computing control strategies in a modular neural network architecture

Sanjay Sharma, George Irwin, M.O. Tokhi, Seán McLoone

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Modelling and control of nonlinear dynamical systems is a challenging problem since the dynamics of such systems change over their parameter space. Conventional methodologies for designing nonlinear control laws, such as gain scheduling, are effective because the designer partitions the overall complex control into a number of simpler sub-tasks. This paper describes a new genetic algorithm based method for the design of a modular neural network (MNN) control architecture that learns such partitions of an overall complex control task. Here a chromosome represents both the structure and parameters of an individual neural network in the MNN controller and a hierarchical fuzzy approach is used to select the chromosomes required to accomplish a given control task. This new strategy is applied to the end-point tracking of a single-link flexible manipulator modelled from experimental data. Results show that the MNN controller is simple to design and produces superior performance compared to a single neural network (SNN) controller which is theoretically capable of achieving the desired trajectory. (C) 2003 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)395-405
Number of pages11
JournalEngineering Applications of Artificial Intelligence
Volume16
Issue number5-6
DOIs
Publication statusPublished - Aug 2003

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'Learning soft computing control strategies in a modular neural network architecture'. Together they form a unique fingerprint.

Cite this