Fuzzy-grey relational optimization for active vibration control in smart composite beams: a multi-objective framework with experimental validation

Yogeesh  N; Markala  Karthik; N  Raja; Asokan  Vasudevan; Rashmi  M; Ashalatha  K S

doi:10.59400/sv3496

Fuzzy-grey relational optimization for active vibration control in smart composite beams: a multi-objective framework with experimental validation

Yogeesh N Department of Electrical and Electronics Engineering, SR University, Warangal 506371, India; Department of Mathematics, Government First Grade College, Tumkur 572101, India
Markala Karthik Department of Electrical and Electronics Engineering, SR University, Warangal 506371, India
N Raja Department of Visual Communication, Sathyabama Institute of Science and Technology, Chennai 600119, India
Asokan Vasudevan Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia; Faculty of Business and Communications, Wekerle Business School, 1083 Budapest, Hungary
Rashmi M Department of Computer Science, Government First Grade College, Bengaluru 560057, India
Ashalatha K S Department of Mathematics, Vedavathi Government First Grade College, Hiriyur 577598, India

Article ID: 3496

Keywords: fuzzy logic; grey relational analysis (GRA); adaptive Linear Quadratic Regulator (adaptive LQR); multi-objective optimization; piezoelectric actuator-sensor; vibration attenuation; control energy efficiency

Abstract

This paper presents a hybrid fuzzy logic–grey relational analysis (Fuzzy–GRA) framework for multi-objective optimization of active vibration control (AVC) in smart composite beams. A fuzzy-adaptive Linear Quadratic Regulator (LQR) is developed, in which the LQR weighting matrices are adjusted online based on a grey relational grade that synthesizes vibration attenuation, control energy, and robustness metrics. A finite-element model incorporating piezoelectric actuator–sensor coupling is used to generate a hypothetical modal-test dataset, and both numerical simulations and laboratory experiments on an aluminium cantilever beam validate the method. Simulation results show up to 25 % and 13.6 % improvements in vibration attenuation over deterministic and genetic-algorithm-tuned LQR, respectively, while reducing control energy by 12.5 %. Experimental trials confirm 29.7 % and 14.3 % attenuation gains, 15.3 % energy savings, and a 41.7 % enhancement in robustness under ± 10 % parameter variations. Environmental robustness tests demonstrate only a 2.1 % performance drop under a 20 °C temperature increase, compared to an 8.1 % drop for conventional tuning. One-way ANOVA confirms that the observed improvements are highly significant (F ≫ F₍₂,₁₂,₀.₀₅₎). The proposed Fuzzy–GRA approach thus offers a mathematically rigorous yet practical strategy for tuning AVC gains under uncertainty, with promising applications in structural health monitoring and precision engineering.

Published

2025-08-31

How to Cite

N, Y., Karthik, M., Raja, N., Vasudevan, A., M, R., & K S, A. (2025). Fuzzy-grey relational optimization for active vibration control in smart composite beams: a multi-objective framework with experimental validation. Sound & Vibration, 59(4). https://doi.org/10.59400/sv3496

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Vol. 59 No. 4 (2025)

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

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