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Nonlinear Generalized Predictive Control Approach for Challenging Processes

[+] Author Affiliations
Ma’moun Abu-Ayyad

Penn State Harrisburg, Harrisburg, PA

Lakshamirinyan Chinta Venkateswararao

University of Toronto, Toronto, ON, Canada

Rickey Dubay

University of New Brunswick, Fredericton, NB, Canada

Paper No. SMASIS2009-1212, pp. 335-341; 7 pages
doi:10.1115/SMASIS2009-1212
From:
  • ASME 2009 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Volume 1: Active Materials, Mechanics and Behavior; Modeling, Simulation and Control
  • Oxnard, California, USA, September 21–23, 2009
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-4896-8 | eISBN: 978-0-7918-3857-0
  • Copyright © 2009 by ASME

abstract

This paper presents the implementation of the fundamental concept of the infinite modeling methodology to the generalized predictive control (GPC) algorithm. This method was termed as infinite modeling generalized predictive control (IMGPC) which uses the nonlinear characteristics of the process such as the process gain and time constant to recalculate the dynamic matrix every sampling instant. Computer simulations were performed on nonlinear plants with different degrees of nonlinearity demonstrating that the infinite modeling approach is readily implemented providing improved control performance comparing to the original structure of GPC. Practical work included real-time control application on a steel cylinder temperature control system. Simulation and experimental results demonstrate that the methodology of infinite modeling is applicable to other advanced control strategies making the methodology generic.

Copyright © 2009 by ASME

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