Internal iteration gradient estimator based parameter identification for nonlinear sandwich system subject to quantized sensor and friction nonlinearity.

ABSTRACT: This study proposes an internal iteration scalar-innovation gradient estimation method based on multi-innovation theory for a nonlinear sandwich system subject to a quantized sensor and friction nonlinearity. Different from the existing multi-innovation gradient method (MISG ), the proposed method is designed to resolve the existing shortages of the conventional MISG. First, the decomposing method is applied to derive the identification model, and the redundant parameter estimation problem is avoided. Then, an adaptive filter based on the prior knowledge of the system is proposed to obtain the helpful identification data. Second, to solve the multi-innovation length problem in MISG, the internal iteration principle is presented to convert the multi-innovation updating to scalar-innovation

SUBMITTER: Lei H 

PROVIDER: S-EPMC12040282 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature