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Combined estimation of the parameters and states for a multivariable state‐space system in presence of colored noise
Author(s) -
Cui Ting,
Chen Feiyan,
Ding Feng,
Sheng Jie
Publication year - 2020
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3101
Subject(s) - kalman filter , multivariable calculus , colors of noise , recursive least squares filter , state space representation , estimation theory , colored , state space , control theory (sociology) , least squares function approximation , computer science , noise (video) , algorithm , state (computer science) , fast kalman filter , extended kalman filter , identification (biology) , mathematics , engineering , artificial intelligence , statistics , control engineering , noise reduction , adaptive filter , composite material , botany , biology , materials science , image (mathematics) , control (management) , estimator
Summary This article addresses the combined estimation issues of parameters and states for multivariable systems in the state‐space form disturbed by colored noises. By utilizing the Kalman filtering principle and the coupling identification concept, we derive a Kalman filtering based partially coupled recursive generalized extended least squares (KF‐PC‐RGELS) algorithm to jointly estimate the parameters and the states. Using the past and the current data in parameter estimation, we propose a Kalman filtering based multi‐innovation partially coupled recursive generalized extended least‐squares algorithm to enhance the parameter estimation accuracy of the KF‐PC‐RGELS algorithm. Finally, a simulation example is provided to test and compare the performance of the proposed algorithms.