Controlling a complex electromechanical process on the basis of a neurofuzzy approach

Authors:
Rodolfo E. Haber;J. R. Alique;A. Alique;R. H. Haber
Affiliations:
Instituto de Automática Industrial (CSIC) and Escuela Politécnica Superior, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, Ctra. de Colmenar Viejo, km 15, 2804 ...;Instituto de Automática Industrial (CSIC), km 22, 800 N-III, La Poveda, 28500 Madrid, Spain;Instituto de Automática Industrial (CSIC), km 22, 800 N-III, La Poveda, 28500 Madrid, Spain;Departamento de Control Automático, Universidad de Oriente, Santiago de Cuba, Cuba
Venue:
Future Generation Computer Systems
Year:
2005

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Abstract

This paper shows the viability of implementing a control strategy based on the internal-model control paradigm, which is a useful synergy of a dynamic ANN trained from real-life data and used to predict process output and a fuzzy-logic control (FLC) that enhances the control system's overall performance. A force control problem involving a complex electromechanical system, represented here by the machining process, is considered as a case study. The main goal is to control a single-output variable, cutting force, by changing a single-input variable, feed rate. The proposed neurofuzzy-control (NFC) scheme consists of a dynamic model using ANNs to estimate process output, and a fuzzy-logic controller (FLC) with the same static gain as the inverse model to determine the control inputs (feed rate) necessary to keep the cutting force constant. Four approaches, the fuzzylogic controller (FLC), the direct inverse controller based on ANNs (DIC-NN), the internal-model controller (IMC-NN) and a neurofuzzy controller (NFC), are simulated and their performances are assessed in terms of several performance measurements. The results demonstrate that the NFC strategy provides better disturbance rejection than the IMC-NN and the FLC for the cases analyzed.