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Fast Nonlinear Predictive Control Using Classical and Parallel Wiener Models: A Comparison for a Neutralization

Robert Nebeluk1, Maciej Ławryńczuk1

  • 1Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland.

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|December 9, 2023
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Summary
This summary is machine-generated.

Parallel Wiener models enhance Model Predictive Control (MPC) prediction accuracy and control quality. This study introduces a fast MPC algorithm using parallel Wiener models, showing improved performance in a neutralization benchmark process.

Keywords:
model predictive controlneutralization reactorwiener models

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Area of Science:

  • Chemical Engineering
  • Control Systems Engineering
  • Computational Modeling

Background:

  • The Wiener model, a series combination of linear and nonlinear blocks, is crucial for prediction in Model Predictive Control (MPC).
  • The parallel Wiener model structure offers potential improvements in modeling accuracy and MPC control quality over the classical series configuration.

Purpose of the Study:

  • To present a fast Model Predictive Control (MPC) algorithm utilizing parallel Wiener models for online prediction.
  • To investigate the impact of model structure on modeling accuracy using a neutralization benchmark process.
  • To evaluate the closed-loop control efficiency of parallel Wiener models within an MPC framework.

Main Methods:

  • Development of a fast MPC algorithm featuring online trajectory linearization for computationally efficient quadratic optimization.
  • Comparative analysis of classical and parallel Wiener model structures in open-loop mode for a neutralization benchmark.
  • Validation of closed-loop MPC performance using parallel Wiener models on the neutralization process.

Main Results:

  • The parallel Wiener model structure demonstrated significantly lower prediction errors in open-loop mode compared to the classical Wiener model.
  • In closed-loop MPC, parallel Wiener models showed improved control quality indicators for the neutralization process.
  • While improvements were observed, the difference in control quality between classical and parallel Wiener models was not statistically significant.

Conclusions:

  • Parallel Wiener models offer enhanced prediction accuracy and potential for improved control in MPC applications.
  • The developed fast MPC algorithm effectively leverages parallel Wiener models for online prediction and optimization.
  • Further research may be needed to fully elucidate the significance of control quality differences in closed-loop systems.