Optimal Model Order Reduction Technique for Dynamic Motor Vehicle Suspension Systems

Kife I. Bin Iqbal, Md. Sumon Hossain, Mohammed Forhad Uddin and Md. Shariful Islam

Journal of Applied Mathematics, 2026, vol. 2026, 1-17

Abstract: High-fidelity full-vehicle suspension models with hundreds to thousands of states are computationally prohibitive for real-time control and virtual prototyping. Existing model reduction techniques distribute approximation effort uniformly across the entire frequency spectrum, producing unnecessarily large errors in the (6–10)-Hz band where human sensitivity to vertical acceleration is highest. This paper introduces the frequency-limited two-sided iterative algorithm, a novel H2-optimal projection framework that concentrates reduction accuracy within a user-defined comfort-relevant interval ω1,ω2 by embedding frequency-weighting filters analytically into the two-sided iteration, thereby avoiding the expensive Gramian computations required by frequency-limited balanced truncation. The framework is validated on a benchmark 1000-state full-vehicle lumped-parameter model that incorporates a flexible chassis with vibrational modes, wheels with asymmetric left-side bump excitation, and detailed driver/passenger seat dynamics. A 23-state FLTSIA reduced model achieves markedly lower errors than IRKA, TSIA, FLBT, and FLIRKA within the prescribed [5, 15] rad/s band, reduces CPU simulation time, and shrinks memory usage. These results demonstrate that FLTSIA enables reliable real-time active suspension control and streamlined virtual prototyping for next-generation vehicle platforms.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljam:3135257

DOI: 10.1155/jama/3135257

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