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Optimal Controller Identification for multivariable non-minimum phase systems.

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  • 1GIPSA-Lab, Université Grenoble Alpes, Grenoble, France.

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|July 30, 2024
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Summary
This summary is machine-generated.

This study presents a data-driven control method for non-minimum phase (NMP) systems, enabling controller design without a plant model. The approach successfully identifies NMP transmission zeros and optimal controller parameters using input-output data.

Keywords:
Data-driven controlModel reference controlNon-minimum phase systemsOCI method

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Area of Science:

  • Control Engineering
  • System Identification
  • Data-Driven Control

Background:

  • Designing controllers for unknown systems is challenging.
  • Non-minimum phase (NMP) systems pose specific difficulties in control design.
  • Model Reference approaches require adaptation for data-driven NMP system control.

Purpose of the Study:

  • To develop a data-driven control strategy for non-minimum phase (NMP) systems.
  • To adapt the Optimal Controller Identification (OCI) method for NMP systems.
  • To identify NMP transmission zeros and controller parameters from input-output data.

Main Methods:

  • Utilizing a Model Reference paradigm with a transfer function matrix as the reference model.
  • Adapting the Optimal Controller Identification (OCI) formulation for data-driven NMP control.
  • Employing a convenient reference model parametrization and flexible performance criterion.

Main Results:

  • Successfully identified NMP transmission zeros of the plant.
  • Determined optimal controller parameters for NMP systems.
  • Demonstrated effectiveness through simulation examples for both diagonal and block-triangular reference models.

Conclusions:

  • The proposed data-driven method effectively addresses control challenges in NMP systems.
  • The adapted OCI method allows for identification of critical NMP system characteristics.
  • This approach offers a viable solution for controller design when plant models are unknown.