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Slime Mould Optimization-Based Approximants of Large-Scale Linear-Time-Invariant Continuous-Time Systems with Assured Stability

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Abstract

In this paper, a novel hybrid model reduction method is presented to simplify a complex, large-scale continuous-time system using the slime mould optimization algorithm (SMOA). The proposed method ensures the stability of reduced-order approximants as stability equations are incorporated along with the SMOA. It is also demonstrated that the stability claim of some of the existing model reduction methods is incorrect. An extensive comparative analysis of the dynamic responses and performance indices is also shown by using two case studies, confirming the supremacy of the presented method over the existing methods.

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Authors and Affiliations

  1. Electrical Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

    Chhabindra Nath Singh, Deepak Kumar & Paulson Samuel

  2. Electrical Engineering Department, BN College of Engineering and Technology, Lucknow, India

    Akhilesh Kumar Gupta

Authors
  1. Chhabindra Nath Singh
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  2. Deepak Kumar
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  3. Paulson Samuel
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  4. Akhilesh Kumar Gupta
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Correspondence to Deepak Kumar.

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Singh, C.N., Kumar, D., Samuel, P. et al. Slime Mould Optimization-Based Approximants of Large-Scale Linear-Time-Invariant Continuous-Time Systems with Assured Stability. Circuits Syst Signal Process 42, 1419–1437 (2023). https://doi.org/10.1007/s00034-022-02153-w

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  • DOI: https://doi.org/10.1007/s00034-022-02153-w

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