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ARX model decomposed on Meixner-Like orthonormal bases.

Safa Maraoui1, Kais Bouzrara1

  • 1Research Laboratory of Automatic, signal processing and Image (LARATSI) ,National School of Engineers of Monastir (ENIM), University of Monastir, Tunisia.

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|June 1, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel ARX model using Meixner-Like filters for enhanced system modeling. The new ARX-Meixner-Like (ARXM-L) approach optimizes system parameters for improved control applications.

Keywords:
ARX modelGenetic algorithmsMPC controlMeixner-like basisNewton–Raphson methodPoles optimization

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Area of Science:

  • Control Systems Engineering
  • System Identification
  • Signal Processing

Background:

  • Traditional linear system modeling often relies on input-only filtering techniques.
  • Existing Autoregressive with Exogenous Inputs (ARX) models can be enhanced for better performance.
  • Meixner-Like (M-L) filters offer a promising approach for signal processing in dynamic systems.

Purpose of the Study:

  • To develop a new modeling approach for linear slowly starting systems.
  • To extend input-only filtering to both input and output filtering using M-L filters within an ARX framework.
  • To propose an optimized ARX model with M-L bases for improved system representation and control.

Main Methods:

  • Developed a novel ARX model incorporating two Meixner-Like (M-L) bases for input and output parameterization.
  • Optimized M-L poles using Newton-Raphson (N-R) and Genetic Algorithms (GA) for optimal system representation.
  • Proposed a new Model Predictive Control (MPC) strategy, termed ARX-Meixner-Like (ARXM-L), based on the optimized model.

Main Results:

  • Successfully demonstrated the ARXM-L modeling approach on a numerical system with delay.
  • Validated the model's effectiveness through three experimental studies: supersonic jet engine inlet, Process Trainer PT326, and a Quanser aero experiment.
  • The optimized M-L poles significantly improved the accuracy of the ARX model representation.

Conclusions:

  • The proposed ARXM-L modeling technique provides an effective extension for linear slowly starting systems.
  • The integration of M-L filters and GA/N-R optimization yields a robust and accurate system model.
  • The ARXM-L model shows significant potential for advanced control applications, as evidenced by experimental validations.