Open AccessParameter estimation for systems with structural uncertainties based on quantised inputs and binary‐valued output observations
Author(s) -
Guo Jin,
Diao JingDong
Publication year - 2018
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2017.1075
Subject(s) - upper and lower bounds , binary number , estimation theory , mathematics , limit (mathematics) , probabilistic logic , function (biology) , measure (data warehouse) , control theory (sociology) , noise (video) , system identification , identification (biology) , linear system , algorithm , mathematical optimization , computer science , statistics , mathematical analysis , botany , arithmetic , control (management) , database , evolutionary biology , artificial intelligence , image (mathematics) , biology
This study investigates the parameter estimation with general quantised inputs and binary‐valued output observations for systems with structural uncertainties of the time‐dependent bias and the non‐linear model mismatch. Combining the empirical‐measure‐based technique and the least‐squares optimisation, an identification algorithm is proposed by use of the output threshold information and the distribution function of the system noise. It is shown that the estimate of the unknown parameter can be sandwiched in between two constructed auxiliary sequences, whose limits are just a lower bound of the limit inferior of the algorithm and an upper bound of the limit superior, respectively. Moreover, probabilistic estimation error bounds are given. Numerical simulations are presented to illustrate the main theoretical results.