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Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach.

Mohammad Fathi1, Hossein Bolandi1

  • 1Electrical Engineering Department, Iran University of Science and Technology, Narmak, Tehran, Iran.

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|February 23, 2024
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Summary
This summary is machine-generated.

This study introduces a novel GA-based clustering method for Multiple Model Control (MMC) systems. It optimizes the number and distribution of local models for enhanced system performance.

Keywords:
Automatic clusteringGenetic algorithmMultiple model controlOptimal model bank

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Area of Science:

  • Control Engineering
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Multiple Model Control (MMC) uses local models for complex systems.
  • Challenges include determining the optimal number and distribution of these models.
  • Effective MMC model banks are crucial for system performance.

Purpose of the Study:

  • To develop an optimal model bank for MMC systems.
  • To address challenges in model number and distribution.
  • To improve MMC system performance through optimal model bank formation.

Main Methods:

  • A GA-based automatic clustering method is proposed.
  • A novel unsupervised algorithm is designed for optimal model bank determination.
  • Mapping MMC concepts to automatic clustering techniques.

Main Results:

  • The proposed method forms a global optimal model bank.
  • It avoids local optima, irrespective of initial conditions.
  • Demonstrated satisfactory performance on spacecraft attitude dynamics.

Conclusions:

  • The GA-based clustering method effectively optimizes MMC model banks.
  • The approach offers a robust solution for complex MIMO, non-linear systems.
  • Validated through a spacecraft attitude dynamics case study.