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<p>The presented research article highlights the systematic approach of weight calculation for determination of approximated order model (AOM) for higher order (HO) underwater robotic autonomous submarine (URAS) system. The AOM for URAS system is determined by employing expansion parameters of URAS system and its desired URAS AOM. In this determination, two-point matching (TPM) between expansion parameters of URAS system and its desired URAS AOM is performed. For the minimization of errors between expansion parameters of URAS system and its desired URAS AOM, fitness function is formulated. By employing TPM, the improved steady-state characteristics (SSC) and transient characteristics (TC) of desired URAS AOM with respect to its HO URAS system can be achieved. For providing suitable importance to SSC and TC, systematic weight calculation (SWC) approach based proximity index value (PIV) method is incorporated. After assigning appropriate weights in fitness function using PIV method, comparatively better URAS AOM is ascertained by exploiting greywolf optimization algorithm (GOA). While obtaining AOM, two crucial constraints are also considered to solve the fitness function using GOA. These constraints are ensuring stable URAS model, and confirming zero steady-state error. In support of URAS AOM, by comparing proposed URAS AOM with benchmark approximation approaches, the analytically presented results and findings are demonstrated in the form of responses and data tabulation.</p>

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PIV method assisted intelligent approximation of underwater robotic autonomous submarine system leveraging expansion parameters

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  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Umesh Kumar Yadav,
  • V. P. Singh,
  • Luigi Fortuna,
  • Umesh Kumar Sahu

摘要

The presented research article highlights the systematic approach of weight calculation for determination of approximated order model (AOM) for higher order (HO) underwater robotic autonomous submarine (URAS) system. The AOM for URAS system is determined by employing expansion parameters of URAS system and its desired URAS AOM. In this determination, two-point matching (TPM) between expansion parameters of URAS system and its desired URAS AOM is performed. For the minimization of errors between expansion parameters of URAS system and its desired URAS AOM, fitness function is formulated. By employing TPM, the improved steady-state characteristics (SSC) and transient characteristics (TC) of desired URAS AOM with respect to its HO URAS system can be achieved. For providing suitable importance to SSC and TC, systematic weight calculation (SWC) approach based proximity index value (PIV) method is incorporated. After assigning appropriate weights in fitness function using PIV method, comparatively better URAS AOM is ascertained by exploiting greywolf optimization algorithm (GOA). While obtaining AOM, two crucial constraints are also considered to solve the fitness function using GOA. These constraints are ensuring stable URAS model, and confirming zero steady-state error. In support of URAS AOM, by comparing proposed URAS AOM with benchmark approximation approaches, the analytically presented results and findings are demonstrated in the form of responses and data tabulation.