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<p>The increasing demand for mechatronic applications operating under adverse conditions has driven the development of new controller tuning strategies, such as the indirect adaptive controller tuning approach (IACTA), utilizing well-known population-based bio-inspired algorithms. While this tuning strategy has significantly managed parametric uncertainties and external disturbances, the exhaustive search inherent in IACTA results in a high computational burden, limiting its application to relatively simple systems. Attempts to reduce this computational load have often led to a trade-off, sacrificing system performance for time efficiency. In this work, we introduce a variant of IACTA that addresses these limitations using a proposed online micro-bio-inspired algorithm, <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="521_2025_11050_Article_IEq1.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\mu\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>IACTA/ODE-OPSO. This new approach is applied to tuning a BLDC motor controller under adverse conditions. Our analysis demonstrates that the <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="521_2025_11050_Article_IEq1.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\mu\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>IACTA/ODE-OPSO outperforms traditional population-based algorithms and other micro-bio-inspired algorithms. Specifically, the proposed method enhances control system performance by up to 1.58% while reducing the time required to solve adaptive tuning intervals by up to 10.62%.</p>

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Micro-bio-inspired metaheuristics for optimized adaptive controller tuning: enhancing BLDC motor performance

  • 期刊论文

  • 发表日期：

    2025年4月6日

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  • Alam Gabriel Rojas-López,
  • Miguel Gabriel Villarreal-Cervantes,
  • Alejandro Rodríguez-Molina,
  • Irving Luna-Ortiz

摘要

The increasing demand for mechatronic applications operating under adverse conditions has driven the development of new controller tuning strategies, such as the indirect adaptive controller tuning approach (IACTA), utilizing well-known population-based bio-inspired algorithms. While this tuning strategy has significantly managed parametric uncertainties and external disturbances, the exhaustive search inherent in IACTA results in a high computational burden, limiting its application to relatively simple systems. Attempts to reduce this computational load have often led to a trade-off, sacrificing system performance for time efficiency. In this work, we introduce a variant of IACTA that addresses these limitations using a proposed online micro-bio-inspired algorithm, \(\mu\) μ IACTA/ODE-OPSO. This new approach is applied to tuning a BLDC motor controller under adverse conditions. Our analysis demonstrates that the \(\mu\) μ IACTA/ODE-OPSO outperforms traditional population-based algorithms and other micro-bio-inspired algorithms. Specifically, the proposed method enhances control system performance by up to 1.58% while reducing the time required to solve adaptive tuning intervals by up to 10.62%.