Skip to content
DraconiticResearch
Search Sources API
203,948 studies31 sourcesUpdated 36 min ago
Peer-reviewedCC BY 4.0PLOS One

Hybrid RBF neural network–based sliding mode control for a magnetorheological elastomer isolator under variable frequency and amplitude excitations

  • Kiran Katari1
  • Umanath R. Poojary2
  • Ananda Hegde
  • Gangadharan K. V.
  1. 1Department of Mechanical Engineering, National Institute of Technology, Mangalore, Karnataka, India
  2. 2Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Udupi, Karnataka, India
Open at sourcePDFDOICite

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.

Related

  1. Tunable Magnetorheological Elastomer Substrates for Studying Mechano-Genetic Interactions in Hypertrophic Cardiomyopathy
    Yasaman Kargar Gaz Kooh, Yuwen Sun, Ganesh Malayath +8
    bioRxivPreprintAll rights reservedFull textSystems & synthetic biologybioRxivPMC
  2. Amplitude without phase: interpersonal movement coordination grades inter-brain coupling
    N. Tasnim, R. M. DeLauder, M. Aychman +9
    bioRxivPreprintAll rights reservedFull textNeurosciencebioRxiv
  3. Egg-Stage Daily Temperature Amplitude Constrains Adult Fitness and Population Growth in Tuta absoluta (Meyrick)
    Xiao-Ning An, Yang Li, Yue Ding +6
    InsectsPeer-reviewedCC BYFull textGeneral biologyEurope PMC
  4. Beyond High-Amplitude Events: Amplitude-Resolved Spatial and Temporal ICA Representations in fMRI
    D. N. T. Vu, V. Calhoun
    bioRxivPreprintCC BY-NCFull textNeurosciencebioRxivEurope PMC
  5. Computing the geometry of phase-amplitude coupling: A new moment-based framework
    M. Keshavarzi, U. Goswami
    bioRxivPreprintCC BYFull textNeurosciencebioRxiv

Cite

BibTeXRIS
BibTeX
@article{katari2026hybrid,
  title = {{Hybrid RBF neural network–based sliding mode control for a magnetorheological elastomer isolator under variable frequency and amplitude excitations}},
  author = {Kiran Katari and Umanath R. Poojary and Ananda Hegde and Gangadharan K. V.},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0360069},
  url = {https://doi.org/10.1371/journal.pone.0360069},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Hybrid RBF neural network–based sliding mode control for a magnetorheological elastomer isolator under variable frequency and amplitude excitations
AU  - Kiran Katari
AU  - Umanath R. Poojary
AU  - Ananda Hegde
AU  - Gangadharan K. V.
PY  - 2026
DA  - 2026/10/09
JO  - PLOS One
DO  - 10.1371/journal.pone.0360069
UR  - https://doi.org/10.1371/journal.pone.0360069
AB  - 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.
SN  - 1932-6203
ER  -