Low-order control-oriented modeling of piezoelectric actuator using Huberian function with low threshold: pseudolinear and neural network models

DOI10.1007/s11071-016-2733-1zbMath1355.93046OpenAlexW2317574746MaRDI QIDQ347300

Hector Manuel Romero Ugalde, Christophe Corbier

Publication date: 30 November 2016

Published in: Nonlinear Dynamics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11071-016-2733-1

zbMATH Keywords

neural network modelnonlinear system identificationHuberian functionpiezoelectric actuatorpseudolinear black box modelvibration drilling control

Mathematics Subject Classification ID

System identification (93B30) Nonlinear systems in control theory (93C10) Vibrations in dynamical problems in solid mechanics (74H45) Electromagnetic effects in solid mechanics (74F15)

Related Items (3)

Fractional order neural networks for system identification ⋮ Articulated estimator random field and geometrical approach applied in system identification ⋮ Design of normalized fractional adaptive algorithms for parameter estimation of control autoregressive autoregressive systems

Cites Work

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• Experimental comparison research on active vibration control for flexible piezoelectric manipulator using fuzzy controller
• Optimal experiment design for dynamic system identification
• Mixed \(L_p\) estimators variety for model order reduction in control oriented system identification
• Identification For Control: Optimal Input Design With Respect To A Worst-Case $\nu$-gap Cost Function
• Extension of the tuning constant in the Huber's function for robust modeling of piezoelectric systems
• Robust Statistics

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