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Zakaria Lammouchi1, Chouaib Labiod2, Kamel Srairi3

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Summary

This study introduces an improved adaptive integral sliding mode observer based Model-Free Predictive Control (AISMO-MFPC) for power electronics. This novel approach enhances system performance by reducing current ripple and harmonics, even with parameter mismatches.

Keywords:
Active vector execution time AVETAdaptive integrated sliding mode observe AISMOCost-functionExperimental validationsFinite states model predictive controller FS-MPCModel-free predictive control MFPCTwo level inverter inverter2L-VSIUltra-local model ULM

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Area of Science:

  • Power Electronics
  • Control Systems Engineering

Background:

  • Model-Free Predictive Control (MFPC) is increasingly applied to power electronic systems.
  • Traditional MFPC using ultra-local models (ULM) often relies on approximate values for non-physical factors, conflicting with MFPC principles.

Purpose of the Study:

  • To propose an improved adaptive integral sliding mode observer based MFPC (AISMO-MFPC) for power electronic systems.
  • To enhance MFPC by accurately estimating unknown factors in the ULM.
  • To reduce current ripple and improve system response using Active Vector Execution Time (AVET).

Main Methods:

  • An improved adaptive integral sliding mode observer (AISMO) is developed to estimate unknown factors in the ULM.
  • Active Vector Execution Time (AVET) is incorporated, utilizing two vectors per sampling period to minimize a cost function.
  • ULM is employed for AVET calculation to simplify control implementation.

Main Results:

  • The proposed AISMO-MFPC effectively estimates unknown factors, enabling independent control of system parameters.
  • Integration of AVET significantly reduces current ripple and harmonic content.
  • The combined approach demonstrates faster system response, particularly under parameter mismatch conditions.

Conclusions:

  • The developed AISMO-MFPC with AVET offers superior performance in power electronic systems.
  • The method provides robust control and improved efficiency, validated by simulations and experiments.