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Nonfragile sampled‐data H ∞ control design for high‐speed train with parametric uncertainties
Author(s) -
Subramanian K.,
Muthukumar P.,
Trinh Hieu
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5330
Subject(s) - control theory (sociology) , parametric statistics , displacement (psychology) , attenuation , stability (learning theory) , control (management) , computer science , state (computer science) , sampling (signal processing) , mathematics , algorithm , statistics , physics , psychology , filter (signal processing) , machine learning , artificial intelligence , computer vision , optics , psychotherapist
This study proposes the nonfragile sampled‐data H ∞ control design for a high‐speed train (HST) with parametric uncertainties. Unlike the existing studies, the HST model is formulated together with the damping force, parametric uncertainties, and uncertain factors. To guarantee the desired tracking speed of HST, and at the equilibrium state, the stability of relative spring displacement between two adjacent carriages, sufficient conditions are addressed under nonfragile sampled‐data H ∞ control based on the actual sampling pattern with an input delay approach. Meanwhile, a H ∞ disturbance attenuation performance is obtained for the uncertain factors, which ensures the safety and comfort of operating the HST. By utilizing experimental Japan Shinkansen HST parameter values, numerical examples are employed to verify the proposed method.