Performance indexes.
<div><p>This paper suggests a novel optimal inverse Radial Basis Function (RBF) neural network model for the control of Twin Rotor Aerodynamic Systems (TRAS), such as Multi-Input–Multi-Output (MIMO) systems with high nonlinearity and coupling effects between channels. After analyzing and...
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| مؤلفون آخرون: | , , |
| منشور في: |
2025
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| _version_ | 1852020266612293632 |
|---|---|
| author | Ahmad Al-Talabi (21387479) |
| author2 | Taqwa Oday Fahad (21387482) Aqeel Abdulazeez Mohammed (21387485) Ali Hussien Mary (21387488) |
| author2_role | author author author |
| author_facet | Ahmad Al-Talabi (21387479) Taqwa Oday Fahad (21387482) Aqeel Abdulazeez Mohammed (21387485) Ali Hussien Mary (21387488) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmad Al-Talabi (21387479) Taqwa Oday Fahad (21387482) Aqeel Abdulazeez Mohammed (21387485) Ali Hussien Mary (21387488) |
| dc.date.none.fl_str_mv | 2025-05-19T17:39:13Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0322999.t005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Performance_indexes_/29101994 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neuroscience Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified thereby creating vertical slower rise time simulation results showed proportional derivative controller particle swarm optimization integral performance indices faster settling time faster rise time neural network model input &# 8211 yaw model shows pitch model exhibits fractional order pid yaw model pitch model input voltage dynamic model xlink "> unknown parameters paper suggests notably reduced modeled using lower overshoot inverse modeling high nonlinearity gaussian function different conditions coupling effects aso ). |
| dc.title.none.fl_str_mv | Performance indexes. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>This paper suggests a novel optimal inverse Radial Basis Function (RBF) neural network model for the control of Twin Rotor Aerodynamic Systems (TRAS), such as Multi-Input–Multi-Output (MIMO) systems with high nonlinearity and coupling effects between channels. After analyzing and linearizing the dynamic model, TRAS is decoupled into two Single Input Single Output (SISO) systems, thereby creating vertical (pitch model) and horizontal (yaw model) systems. The relationship between the output angle of each subsystem and the input voltage is modeled using the inverse RBF neural network. The weights, biases, centers and widths of the Gaussian function are unknown parameters of the proposed inverse neural model, and they are obtained using Atom Search Optimization (ASO). A combination of the proportional derivative controller and the proposed inverse neural model fed forward controller is then applied to control the angles of each subsystem with different conditions. The simulation results showed that the proposed controller demonstrates noticeable performance improvements over the Fractional Order PID (FOPID) and Particle Swarm Optimization-PID (PSO-PID) controllers. Compared to FOPID, it achieves an 88.3% faster rise time, a 96.0% faster settling time, and a 93.8% lower overshoot for the Yaw model, along with a 42.8% faster rise time, a 73.9% faster settling time, and an 86.8% lower overshoot for the Pitch model. In comparison to PSO-PID, the Yaw model shows a 36.2% faster rise time, an 86.7% faster settling time, and a 59.7% lower overshoot, while the Pitch model exhibits a 58.4% slower rise time but compensates with a 59.9% faster settling time and a 71.2% lower overshoot. Additionally, integral performance indices are notably reduced for the proposed controller.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_9fdb2d79167bcb08136af4b2cc59f485 |
| identifier_str_mv | 10.1371/journal.pone.0322999.t005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29101994 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Performance indexes.Ahmad Al-Talabi (21387479)Taqwa Oday Fahad (21387482)Aqeel Abdulazeez Mohammed (21387485)Ali Hussien Mary (21387488)NeuroscienceScience PolicyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedthereby creating verticalslower rise timesimulation results showedproportional derivative controllerparticle swarm optimizationintegral performance indicesfaster settling timefaster rise timeneural network modelinput &# 8211yaw model showspitch model exhibitsfractional order pidyaw modelpitch modelinput voltagedynamic modelxlink ">unknown parameterspaper suggestsnotably reducedmodeled usinglower overshootinverse modelinghigh nonlinearitygaussian functiondifferent conditionscoupling effectsaso ).<div><p>This paper suggests a novel optimal inverse Radial Basis Function (RBF) neural network model for the control of Twin Rotor Aerodynamic Systems (TRAS), such as Multi-Input–Multi-Output (MIMO) systems with high nonlinearity and coupling effects between channels. After analyzing and linearizing the dynamic model, TRAS is decoupled into two Single Input Single Output (SISO) systems, thereby creating vertical (pitch model) and horizontal (yaw model) systems. The relationship between the output angle of each subsystem and the input voltage is modeled using the inverse RBF neural network. The weights, biases, centers and widths of the Gaussian function are unknown parameters of the proposed inverse neural model, and they are obtained using Atom Search Optimization (ASO). A combination of the proportional derivative controller and the proposed inverse neural model fed forward controller is then applied to control the angles of each subsystem with different conditions. The simulation results showed that the proposed controller demonstrates noticeable performance improvements over the Fractional Order PID (FOPID) and Particle Swarm Optimization-PID (PSO-PID) controllers. Compared to FOPID, it achieves an 88.3% faster rise time, a 96.0% faster settling time, and a 93.8% lower overshoot for the Yaw model, along with a 42.8% faster rise time, a 73.9% faster settling time, and an 86.8% lower overshoot for the Pitch model. In comparison to PSO-PID, the Yaw model shows a 36.2% faster rise time, an 86.7% faster settling time, and a 59.7% lower overshoot, while the Pitch model exhibits a 58.4% slower rise time but compensates with a 59.9% faster settling time and a 71.2% lower overshoot. Additionally, integral performance indices are notably reduced for the proposed controller.</p></div>2025-05-19T17:39:13ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0322999.t005https://figshare.com/articles/dataset/Performance_indexes_/29101994CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291019942025-05-19T17:39:13Z |
| spellingShingle | Performance indexes. Ahmad Al-Talabi (21387479) Neuroscience Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified thereby creating vertical slower rise time simulation results showed proportional derivative controller particle swarm optimization integral performance indices faster settling time faster rise time neural network model input &# 8211 yaw model shows pitch model exhibits fractional order pid yaw model pitch model input voltage dynamic model xlink "> unknown parameters paper suggests notably reduced modeled using lower overshoot inverse modeling high nonlinearity gaussian function different conditions coupling effects aso ). |
| status_str | publishedVersion |
| title | Performance indexes. |
| title_full | Performance indexes. |
| title_fullStr | Performance indexes. |
| title_full_unstemmed | Performance indexes. |
| title_short | Performance indexes. |
| title_sort | Performance indexes. |
| topic | Neuroscience Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified thereby creating vertical slower rise time simulation results showed proportional derivative controller particle swarm optimization integral performance indices faster settling time faster rise time neural network model input &# 8211 yaw model shows pitch model exhibits fractional order pid yaw model pitch model input voltage dynamic model xlink "> unknown parameters paper suggests notably reduced modeled using lower overshoot inverse modeling high nonlinearity gaussian function different conditions coupling effects aso ). |