A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation

Mathematical models are basic for designing controller and system identification is the theory and methods for establishing the mathematical models of practical systems. This paper considers the parameter identification for Hammerstein controlled autoregressive systems. Using the key term separation...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of the Franklin Institute Jg. 355; H. 8; S. 3737 - 3752
Hauptverfasser: Ding, Feng, Chen, Huibo, Xu, Ling, Dai, Jiyang, Li, Qishen, Hayat, Tasawar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elmsford Elsevier Ltd 01.05.2018
Elsevier Science Ltd
Schlagworte:
Algorithms
Computer simulation
Computing time
Control systems design
Identification methods
Least squares
Mathematical models
Multivariate analysis
Nonlinear systems
Parameter estimation
Parameter identification
Product quality
Regression analysis
Separation
System identification
ISSN:0016-0032, 1879-2693, 0016-0032
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Mathematical models are basic for designing controller and system identification is the theory and methods for establishing the mathematical models of practical systems. This paper considers the parameter identification for Hammerstein controlled autoregressive systems. Using the key term separation technique to express the system output as a linear combination of the system parameters, the system is decomposed into several subsystems with fewer variables, and then a hierarchical least squares (HLS) algorithm is developed for estimating all parameters involving in the subsystems. The HLS algorithm requires less computation than the recursive least squares algorithm. The computational efficiency comparison and simulation results both confirm the effectiveness of the proposed algorithms.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2018.01.052