Multi‐innovation gradient‐based iterative identification methods for feedback nonlinear systems by using the decomposition technique

Summary This paper studies the parameter estimation problems of feedback nonlinear systems. Combining the multi‐innovation identification theory with the negative gradient search, we derive a multi‐innovation gradient‐based iterative algorithm. In order to reduce the computational burden and further...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of robust and nonlinear control Vol. 33; no. 13; pp. 7755 - 7773
Main Authors Yang, Dan, Ding, Feng
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 10.09.2023
Subjects
Decomposition
decomposition technique
Feedback
Identification methods
Innovations
Iterative algorithms
iterative identification
Iterative methods
multi‐innovation identification
nonlinear system
Nonlinear systems
Parameter estimation
Parameter identification
Subsystems
Online AccessGet full text

Cover

More Information
Summary:Summary This paper studies the parameter estimation problems of feedback nonlinear systems. Combining the multi‐innovation identification theory with the negative gradient search, we derive a multi‐innovation gradient‐based iterative algorithm. In order to reduce the computational burden and further improve the parameter estimation accuracy, a decomposition multi‐innovation gradient‐based iterative algorithm is proposed by using the decomposition technique. The key is to transform an original system into two subsystems and to estimate the parameters of each subsystem, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed algorithms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6796