Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models
This article proposes an identification method of continuous-time fractional-order Hammerstein model using smoothed functional algorithm with a norm-limited update vector (NL-SFA). In particular, the standard smoothed functional algorithm (SFA) based method is modified by implementing a limit functi...
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| Format: | Article |
| Language: | English |
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Taylor & Francis
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44016/ http://umpir.ump.edu.my/id/eprint/44016/1/Smoothed%20functional%20algorithm%20with%20norm-limited%20update%20vector%20for%20identification.pdf |
| _version_ | 1848827010680356864 |
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| author | Mok, Ren Hao Mohd Ashraf, Ahmad |
| author_facet | Mok, Ren Hao Mohd Ashraf, Ahmad |
| author_sort | Mok, Ren Hao |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This article proposes an identification method of continuous-time fractional-order Hammerstein model using smoothed functional algorithm with a norm-limited update vector (NL-SFA). In particular, the standard smoothed functional algorithm (SFA) based method is modified by implementing a limit function in the update vector of the standard SFA based method to solve the issue of high tendency of divergence during the identification process. As a result of this, the proposed NL-SFA based method is applied to identify the variables of the linear and non-linear subsystems in the Hammerstein model. While most of the actual linear subsystems can be naturally expressed in a continuous-time domain, the implementation of the fractional-order could also reduce the computational complexity in finding a more accurate reduced-order model. Moreover, three experiments of the Hammerstein model identification based on a numerical example, an actual twin-rotor system, and an actual flexible manipulator system were carried out in this study to verify the effectiveness of the proposed NL-SFA-based method. The numerical and experimental results were analyzed to correspond to the measurement of the objective function and variable identification error and time-domain and frequency-domain responses. Conclusively, the proposed NL-SFA-based method can provide stable convergence and significantly better accuracy of the Hammerstein model in the numerical example, the actual twin-rotor system, and the flexible manipulator system compared to the standard SFA. Moreover, the proposed NL-SFA also provides slightly competitive identification accuracy with the existing norm-limited simultaneous perturbation stochastic approximation (NL-SPSA) and the average multi-verse optimizer sine cosine algorithm (AMVO-SCA) based methods. |
| first_indexed | 2025-11-15T03:53:55Z |
| format | Article |
| id | ump-44016 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:53:55Z |
| publishDate | 2024 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-440162025-03-10T01:09:19Z http://umpir.ump.edu.my/id/eprint/44016/ Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models Mok, Ren Hao Mohd Ashraf, Ahmad TK Electrical engineering. Electronics Nuclear engineering This article proposes an identification method of continuous-time fractional-order Hammerstein model using smoothed functional algorithm with a norm-limited update vector (NL-SFA). In particular, the standard smoothed functional algorithm (SFA) based method is modified by implementing a limit function in the update vector of the standard SFA based method to solve the issue of high tendency of divergence during the identification process. As a result of this, the proposed NL-SFA based method is applied to identify the variables of the linear and non-linear subsystems in the Hammerstein model. While most of the actual linear subsystems can be naturally expressed in a continuous-time domain, the implementation of the fractional-order could also reduce the computational complexity in finding a more accurate reduced-order model. Moreover, three experiments of the Hammerstein model identification based on a numerical example, an actual twin-rotor system, and an actual flexible manipulator system were carried out in this study to verify the effectiveness of the proposed NL-SFA-based method. The numerical and experimental results were analyzed to correspond to the measurement of the objective function and variable identification error and time-domain and frequency-domain responses. Conclusively, the proposed NL-SFA-based method can provide stable convergence and significantly better accuracy of the Hammerstein model in the numerical example, the actual twin-rotor system, and the flexible manipulator system compared to the standard SFA. Moreover, the proposed NL-SFA also provides slightly competitive identification accuracy with the existing norm-limited simultaneous perturbation stochastic approximation (NL-SPSA) and the average multi-verse optimizer sine cosine algorithm (AMVO-SCA) based methods. Taylor & Francis 2024 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/44016/1/Smoothed%20functional%20algorithm%20with%20norm-limited%20update%20vector%20for%20identification.pdf Mok, Ren Hao and Mohd Ashraf, Ahmad (2024) Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models. IETE Journal of Research, 70 (2). 1814 -1832. ISSN 0377-2063 (Print); 0974-780X (Online). (Published) https://doi.org/10.1080/03772063.2022.2152879 https://doi.org/10.1080/03772063.2022.2152879 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mok, Ren Hao Mohd Ashraf, Ahmad Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models |
| title | Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models |
| title_full | Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models |
| title_fullStr | Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models |
| title_full_unstemmed | Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models |
| title_short | Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models |
| title_sort | smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order hammerstein models |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/44016/ http://umpir.ump.edu.my/id/eprint/44016/ http://umpir.ump.edu.my/id/eprint/44016/ http://umpir.ump.edu.my/id/eprint/44016/1/Smoothed%20functional%20algorithm%20with%20norm-limited%20update%20vector%20for%20identification.pdf |