Improving Linear State-Space Models with Additional Iterations
Paper in proceeding, 2018

An estimated state-space model can possibly be improved by further iterations with estimation data. This contribution specifically studies if models obtained by subspace estimation can be improved by subsequent re-estimation of the B, C, and D matrices (which involves linear estimation problems). Several tests are performed, which show that it is generally advisable to do such further re-estimation steps using the maximum likelihood criterion. Stated more succinctly in terms of MATLABĀ® functions, ssest generally outperforms n4sid.

subspace identification

parameter estimation

maximum likelihood

state-space models


Suat Gumussoy

MathWorks Inc

Ahmet Azda Ozdemir

MathWorks Inc

Tomas McKelvey

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Lennart Ljung

Linköping University

Mladen Gibanica

Chalmers, Mechanics and Maritime Sciences (M2), Dynamics

Volvo Cars

Rajiv Singh

MathWorks Inc


24058963 (eISSN)

Vol. 51 15 341-346

18th IFAC Symposium on System Identification SYSID 2018
Stockholm, Sweden,

Subject Categories

Control Engineering

Signal Processing



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