Improving Linear State-Space Models with Additional Iterations
Paper i 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 MATLAB® functions, ssest generally outperforms n4sid.

state-space models

maximum likelihood

subspace identification

parameter estimation

Författare

Ahmet Azda Ozdemir

MathWorks

Suat Gumussoy

MathWorks

Tomas McKelvey

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Lennart Ljung

Linköpings universitet

Mladen Gibanica

Chalmers, Mekanik och maritima vetenskaper, Dynamik

Volvo Cars

Rajiv Singh

MathWorks

IFAC-PapersOnLine

2405-8963 (ISSN)

Vol. 51 15 341-346

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

Ämneskategorier

Reglerteknik

Signalbehandling

DOI

10.1016/j.ifacol.2018.09.158

Mer information

Senast uppdaterat

2018-11-07