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A general controller-based dynamic linearized model-free adaptive control and its application to PMLSM.

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Summary
This summary is machine-generated.

This study enhances model-free adaptive control for unknown nonlinear systems using dynamic linearization. The data-driven approach ensures tracking error boundedness with improved mathematical analysis.

Keywords:
Data-driven controlDiscrete-time systemsDynamic linearization methodModel-free adaptive controlNonlinear non-affine systems

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Area of Science:

  • Control Engineering
  • Systems Theory
  • Nonlinear Dynamics

Background:

  • Existing model-free adaptive control (MFAC) methods have limitations for complex systems.
  • Nonlinear, non-affine discrete-time systems pose significant control challenges.
  • Dynamic linearization offers a promising approach for simplifying control design.

Purpose of the Study:

  • To extend and improve controller-based dynamic linearized model-free adaptive control methods.
  • To develop a data-driven control approach for unknown nonlinear non-affine discrete-time systems.
  • To analyze tracking error boundedness under less stringent conditions.

Main Methods:

  • Full-form dynamic linearized data model and controller design.
  • Simplified Newton-type optimization with adaptive estimation algorithms.
  • Mathematical induction, Geršgorin discs, and contraction mapping principle for analysis.

Main Results:

  • The proposed approach integrates compact and partial form models.
  • Improved analysis of ultimate boundedness for tracking error.
  • Demonstrated effectiveness using a permanent magnet linear synchronous motor example.

Conclusions:

  • The enhanced MFAC approach is effective for unknown nonlinear systems.
  • The data-driven method offers robust tracking performance.
  • The approach has potential applications in various engineering fields.