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.
subspace identification
parameter estimation
maximum likelihood
state-space models
Författare
Suat Gumussoy
MathWorks
Ahmet Azda Ozdemir
MathWorks
Tomas McKelvey
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Lennart Ljung
Linköpings universitet
Mladen Gibanica
Chalmers, Mekanik och maritima vetenskaper, Dynamik
Volvo Cars
Rajiv Singh
MathWorks
IFAC-PapersOnLine
24058971 (ISSN) 24058963 (eISSN)
Vol. 51 15 341-346
18th IFAC Symposium on System Identification SYSID 2018
Stockholm, Sweden,
Stockholm, Sweden,
Ämneskategorier
Reglerteknik
Signalbehandling
DOI
10.1016/j.ifacol.2018.09.158