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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Adaptive MPC based on MIMO ARX-Laguerre model.

Imen Ben Abdelwahed1, Abdelkader Mbarek1, Kais Bouzrara1

  • 1Research Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, 5019, Tunisia.

ISA Transactions
|December 13, 2016
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Summary
This summary is machine-generated.

This study introduces an adaptive predictive control method using a simplified MIMO ARX-Laguerre model. This approach enables real-time implementation by avoiding complex optimization, demonstrated on a CSTR process.

Keywords:
ARX-Laguerre modelAdaptive predictive controlMIMO system modelingOnline identificationPole optimization

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Area of Science:

  • Control Engineering
  • Process Systems Engineering
  • Computational Modeling

Background:

  • Model complexity is a significant challenge in implementing advanced control strategies.
  • Traditional linear predictive control often requires computationally intensive optimization algorithms.
  • Real-time adaptive control is crucial for dynamic industrial processes.

Purpose of the Study:

  • To develop a computationally efficient method for synthesizing adaptive predictive controllers.
  • To utilize a reduced-complexity model for improved real-time performance.
  • To validate the proposed method on a standard chemical process benchmark.

Main Methods:

  • A Multi-Input Multi-Output (MIMO) ARX model is projected onto Laguerre bases, creating a reduced-complexity MIMO ARX-Laguerre model.
  • Adaptive predictive control laws are derived using multi-step-ahead finite-element predictors identified from experimental data.
  • Online identification algorithms are employed to tune model parameters and Laguerre poles iteratively.

Main Results:

  • The MIMO ARX-Laguerre model offers an easily representable recursive structure.
  • The proposed control strategy avoids time-consuming numerical optimization, facilitating real-time application.
  • Successful synthesis and simulation of adaptive predictive controllers for the CSTR process benchmark were achieved.

Conclusions:

  • The proposed method provides an efficient and practical approach for real-time adaptive predictive control.
  • Reduced-order modeling using Laguerre bases significantly enhances computational feasibility.
  • The technique is well-suited for controlling complex dynamic systems like the CSTR.