The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jan. 09, 2018
Filed:
Jun. 21, 2012
Jiangang LU, Zhejiang, CN;
Jie You, Zhejiang, CN;
Qinmin Yang, Zhejiang, CN;
Youxian Sun, Zhejiang, CN;
Jiangang Lu, Zhejiang, CN;
Jie You, Zhejiang, CN;
Qinmin Yang, Zhejiang, CN;
Youxian Sun, Zhejiang, CN;
Zhejiang University, Zhejiang, CN;
Abstract
The invention discloses an identification method of nonlinear parameter varying models (NPV) and belongs to the industrial identification field. The invention carries out identification tests and model identification for an identified object with nonlinear parameter varying characteristics. Firstly, the multi-input single-output nonlinear parameter varying model is identified through the steps of local nonlinear model tests, local nonlinear models identification, and operating point variable transition tests; after completing the identification of all the multi-input single-output nonlinear parameter varying models with respect to all the controlled variables, the completed multi-input multi-output nonlinear parameter varying models are built. The nonlinear parameter varying models of an identified object can be obtained by the identification method of the present invention with limited input/output data without detailed mechanism knowledge of the identified object. The nonlinear parameter varying models obtained can be used in model-based control algorithm design and process simulation, as well as in product quality prediction reasoning models and soft sensors.