Data Informativity for the Identification of particular Parallel Hammerstein Systems. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Colin, K.; Bombois, X.; Bako, L.; Morelli, F.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 1102
• Page End: 1107
• Publication Date: 2020
• Subjects: System Identification -- Data Informativity -- Hammerstein Systems -- Prediction Error Method -- Consistency; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1308
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: To obtain a consistent estimate when performing an identification with Prediction Error, it is important that the excitation yields informative data with respect to the chosen model structure. While the characterization of this property seems to be a mature research area in the linear case, the same cannot be said for nonlinear systems. In this work, we study the data informativity for a particular type of Hammerstein systems for two commonly-used excitations: white Gaussian noise and multisine. The real life example of the MEMS gyroscope is considered.