Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane
This paper presents the identification of double pendulum overhead crane (DPOC) plant based on the hybrid Multi-Verse Optimizer with Sine Cosine Algorithm (HMVOSCA) using the continuous-time Hammerstein model. In the HMVOSCA algorithm, the new position updating mechanism of the traditional MVO metho...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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IEEE
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33418/ http://umpir.ump.edu.my/id/eprint/33418/1/Modified%20multi-verse%20optimizer%20for%20nonlinear%20system%20identification_FULL.pdf http://umpir.ump.edu.my/id/eprint/33418/2/Modified%20multi-verse%20optimizer%20for%20nonlinear%20system%20identification.pdf |
| _version_ | 1848824248566546432 |
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| author | Julakha, Jahan Jui Mohd Ashraf, Ahmad Muhammad Ikram, Mohd Rashid |
| author_facet | Julakha, Jahan Jui Mohd Ashraf, Ahmad Muhammad Ikram, Mohd Rashid |
| author_sort | Julakha, Jahan Jui |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This paper presents the identification of double pendulum overhead crane (DPOC) plant based on the hybrid Multi-Verse Optimizer with Sine Cosine Algorithm (HMVOSCA) using the continuous-time Hammerstein model. In the HMVOSCA algorithm, the new position updating mechanism of the traditional MVO method is modified based on the sine function and cosine function which is taken from the Sine Cosine Algorithm (SCA). Moreover, an average position is chosen by computing the mean between the current position and the current best position obtained so far. These modifications are mainly for balancing exploration and exploitation and escaping from local optima and expected better identification accuracy of the DPOC plant. In the Hammerstein model identification, a continuous-time linear subsystem is used, which is more suitable for representing any real plant. The HMVOSCA algorithm is used to tune the linear and nonlinear parameters to reduce the gap between the estimated results and the actual results. The efficiency of the proposed HMVOSCA algorithm is evaluated using the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon's test method. The experimental findings illustrate that the HMVOSCA algorithm can identify a Hammerstein model that generates an estimated output like the actual DPOC system output. Moreover, the identified results also show that the HMVOSCA algorithm outperforms other existing metaheuristics algorithms. |
| first_indexed | 2025-11-15T03:10:01Z |
| format | Conference or Workshop Item |
| id | ump-33418 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:10:01Z |
| publishDate | 2021 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-334182022-04-08T07:08:09Z http://umpir.ump.edu.my/id/eprint/33418/ Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane Julakha, Jahan Jui Mohd Ashraf, Ahmad Muhammad Ikram, Mohd Rashid TK Electrical engineering. Electronics Nuclear engineering This paper presents the identification of double pendulum overhead crane (DPOC) plant based on the hybrid Multi-Verse Optimizer with Sine Cosine Algorithm (HMVOSCA) using the continuous-time Hammerstein model. In the HMVOSCA algorithm, the new position updating mechanism of the traditional MVO method is modified based on the sine function and cosine function which is taken from the Sine Cosine Algorithm (SCA). Moreover, an average position is chosen by computing the mean between the current position and the current best position obtained so far. These modifications are mainly for balancing exploration and exploitation and escaping from local optima and expected better identification accuracy of the DPOC plant. In the Hammerstein model identification, a continuous-time linear subsystem is used, which is more suitable for representing any real plant. The HMVOSCA algorithm is used to tune the linear and nonlinear parameters to reduce the gap between the estimated results and the actual results. The efficiency of the proposed HMVOSCA algorithm is evaluated using the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon's test method. The experimental findings illustrate that the HMVOSCA algorithm can identify a Hammerstein model that generates an estimated output like the actual DPOC system output. Moreover, the identified results also show that the HMVOSCA algorithm outperforms other existing metaheuristics algorithms. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33418/1/Modified%20multi-verse%20optimizer%20for%20nonlinear%20system%20identification_FULL.pdf pdf en http://umpir.ump.edu.my/id/eprint/33418/2/Modified%20multi-verse%20optimizer%20for%20nonlinear%20system%20identification.pdf Julakha, Jahan Jui and Mohd Ashraf, Ahmad and Muhammad Ikram, Mohd Rashid (2021) Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane. In: 19th IEEE Student Conference on Research and Development: Sustainable Engineering and Technology towards Industry Revolution (SCOReD 2021) , 23-25 November 2021 , Kota Kinabalu, Malaysia. pp. 212-217.. ISBN 978-1-6654-0193-7 (Published) https://doi.org/10.1109/SCOReD53546.2021.9652744 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Julakha, Jahan Jui Mohd Ashraf, Ahmad Muhammad Ikram, Mohd Rashid Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane |
| title | Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane |
| title_full | Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane |
| title_fullStr | Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane |
| title_full_unstemmed | Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane |
| title_short | Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane |
| title_sort | modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/33418/ http://umpir.ump.edu.my/id/eprint/33418/ http://umpir.ump.edu.my/id/eprint/33418/1/Modified%20multi-verse%20optimizer%20for%20nonlinear%20system%20identification_FULL.pdf http://umpir.ump.edu.my/id/eprint/33418/2/Modified%20multi-verse%20optimizer%20for%20nonlinear%20system%20identification.pdf |