Improving Linear State-Space Models with Additional Iterations
arXiv:2003.06304 · doi:10.1016/j.ifacol.2018.09.158
Abstract
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 shows 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.
18th IFAC Symposium on System Identification, Stockholm, Sweden, July 9-11, 2018