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...
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| Vydáno v: | Journal of the Franklin Institute Ročník 355; číslo 8; s. 3737 - 3752 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elmsford
Elsevier Ltd
01.05.2018
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0016-0032, 1879-2693, 0016-0032 |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Ding, Feng Chen, Huibo Xu, Ling Dai, Jiyang Hayat, Tasawar Li, Qishen |
| Author_xml | – sequence: 1 givenname: Feng orcidid: 0000-0002-2721-2025 surname: Ding fullname: Ding, Feng email: fding@qust.edu.cn, fding@jiangnan.edu.cn organization: College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China – sequence: 2 givenname: Huibo surname: Chen fullname: Chen, Huibo organization: School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China – sequence: 3 givenname: Ling orcidid: 0000-0002-5040-5634 surname: Xu fullname: Xu, Ling organization: School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China – sequence: 4 givenname: Jiyang surname: Dai fullname: Dai, Jiyang organization: School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, PR China – sequence: 5 givenname: Qishen surname: Li fullname: Li, Qishen organization: School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, PR China – sequence: 6 givenname: Tasawar surname: Hayat fullname: Hayat, Tasawar organization: Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
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| SubjectTerms | 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 |
| Title | A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation |
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