Brief Paper: Simultaneous Constrained Model Predictive Control and Identification of DARX Processes

  • Authors:

  • MANOJ SHOUCHE;HASMET GENCELI;VUTHANDAM PREMKIRAN;MICHAEL NIKOLAOU

  • Affiliations:

  • Department of Chemical Engineering, Texas A&M University, College Station, TX 77843-3122, USA;Department of Chemical Engineering, Texas A&M University, College Station, TX 77843-3122, USA;Department of Chemical Engineering, Texas A&M University, College Station, TX 77843-3122, USA;Department of Chemical Engineering, Texas A&M University, College Station, TX 77843-3122, USA and Department of Chemical Engineering, University of Houston, Houston, TX 77204-4792, USA

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 1998

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Abstract

In this work, we formulate a new approach to simultaneous constrained model predictive control and identification (MPCI). The proposed approach relies on the development of a persistent excitation (PE) criterion for processes described by DARX models. That PE criterion is used as an additional constraint in the standard on-line optimization of MPC. The resulting on-line optimization problem of MPCI is handled by successively solving a series of semi-definite programming problems. Advantages of MPCI in comparison to other closed-loop identification methods are (a) Constraints on process inputs and outputs are handled explicitly, (b) Deterioration of output regulation is kept to a minimum, while closed-loop identification is performed. The applicability of the method is illustrated by a number of simulation studies. Theoretical and computational issues for further investigation are suggested.