{
  "abstract": "Magnetorheological elastomers have attracted significant attention for semi-active vibration isolation applications due to their ability to rapidly change mechanical properties under an applied magnetic field. However, the nonlinear and hysteretic behavior of Magnetorheological elastomer materials poses challenges for achieving robust vibration suppression under varying excitation conditions. This study presents a hybrid control strategy that integrates a radial basis function neural network with a Sliding Mode Controller to enhance the vibration isolation performance of an MRE-based semi-active isolator. A MRE isolator with orthogonal magnetic flux configuration is designed and fabricated, and its dynamic characteristics are evaluated under harmonic excitation in the frequency range of 15–80 Hz. The proposed hybrid controller adaptively estimates system nonlinearities using the neural network while maintaining robustness through sliding mode control. Experimental and simulation results demonstrate that the proposed controller provides effective vibration attenuation while maintaining stable adaptive performance under varying excitation frequency and amplitude. The results show that the semi-active MRE isolator achieves a maximum vibration reduction of 55.8% near the resonance region under the investigated operating conditions, while maintaining stable performance across varying excitation frequencies. The proposed control framework provides an effective solution for handling nonlinear dynamics, and it offers greater adaptability in smart vibration isolation systems based on magnetorheological elastomers.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mechanical Engineering, National Institute of Technology, Mangalore, Karnataka, India",
    "Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Udupi, Karnataka, India"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kiran Katari"
    },
    {
      "affs": [
        1
      ],
      "name": "Umanath R. Poojary"
    },
    {
      "name": "Ananda Hegde"
    },
    {
      "name": "Gangadharan K. V."
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/68rj9bhkc4zjx.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0360069",
  "fulltext_url": null,
  "id": "68rj9bhkc4zjx",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0360069",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2205|Vibration Control and Rheological Fluids"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neural networks"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Behavior"
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      "plos",
      "/Computer and information sciences/Neural networks"
    ],
    [
      "plos",
      "/Computer and information sciences/Systems science/Nonlinear dynamics"
    ],
    [
      "plos",
      "/Computer and information sciences/Systems science/Nonlinear systems"
    ],
    [
      "plos",
      "/Engineering and technology/Mechanical engineering/Vibration engineering"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Material properties/Mechanical properties/Stiffness"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Systems science/Nonlinear dynamics"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Systems science/Nonlinear systems"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Vibration"
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      "/Physical sciences/Physics/Condensed matter physics/Magnetism/Magnetic fields"
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      "plos",
      "/Social sciences/Psychology/Behavior"
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  "openalex": "W7221144648",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0360069&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/68rj9bhkc4zjx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0360069",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0360069"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "chemistry",
      "origin": "plos:materials science",
      "subfield": "materials-polymers"
    },
    {
      "field": "physics",
      "origin": "plos:condensed matter physics",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Hybrid RBF neural network–based sliding mode control for a magnetorheological elastomer isolator under variable frequency and amplitude excitations",
  "title_text": "Hybrid RBF neural network–based sliding mode control for a magnetorheological elastomer isolator under variable frequency and amplitude excitations",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/68rj9bhkc4zjx",
  "venue": "PLOS One",
  "versions": []
}