Design of a multivariable neural controller for control of a nonlinear MIMO plant
The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2014-06-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amcs-2014-0027 |
| _version_ | 1818588742545309696 |
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| author | Bańka Stanisław Dworak Paweł Jaroszewski Krzysztof |
| author_facet | Bańka Stanisław Dworak Paweł Jaroszewski Krzysztof |
| author_sort | Bańka Stanisław |
| collection | DOAJ |
| description | The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship’s current forward speed measured with respect to water and the systematically calculated difference between the course angle and the sea current (yaw angle). Four different methods for synthesis of multivariable modal controllers are used to obtain source data for training the neural controller with parameters reproduced by neural networks. Neural networks are designed on the basis of 3650 modal controllers obtained with the use of the pole placement technique after having linearized the model of LF motions made by the vessel at its nominal operating points in steady states that are dependent on the specified yaw angle and the sea current velocity. The final part of the paper includes simulation results of system operation with a neural controller along with conclusions and final remarks. |
| first_indexed | 2024-12-16T09:29:35Z |
| format | Article |
| id | doaj.art-cfe42b1627ec410c8ff4899c7c7ba6f8 |
| institution | Directory Open Access Journal |
| issn | 2083-8492 |
| language | English |
| last_indexed | 2024-12-16T09:29:35Z |
| publishDate | 2014-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Applied Mathematics and Computer Science |
| spelling | doaj.art-cfe42b1627ec410c8ff4899c7c7ba6f82022-12-21T22:36:35ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922014-06-0124235736910.2478/amcs-2014-0027amcs-2014-0027Design of a multivariable neural controller for control of a nonlinear MIMO plantBańka Stanisław0Dworak Paweł1Jaroszewski Krzysztof2Faculty of Electrical Engineering West Pomeranian University of Technology in Szczecin, 26 Kwietnia 10, 71-126 Szczecin, PolandFaculty of Electrical Engineering West Pomeranian University of Technology in Szczecin, 26 Kwietnia 10, 71-126 Szczecin, PolandFaculty of Electrical Engineering West Pomeranian University of Technology in Szczecin, 26 Kwietnia 10, 71-126 Szczecin, PolandThe paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship’s current forward speed measured with respect to water and the systematically calculated difference between the course angle and the sea current (yaw angle). Four different methods for synthesis of multivariable modal controllers are used to obtain source data for training the neural controller with parameters reproduced by neural networks. Neural networks are designed on the basis of 3650 modal controllers obtained with the use of the pole placement technique after having linearized the model of LF motions made by the vessel at its nominal operating points in steady states that are dependent on the specified yaw angle and the sea current velocity. The final part of the paper includes simulation results of system operation with a neural controller along with conclusions and final remarks.https://doi.org/10.2478/amcs-2014-0027mimo multivariable control systemsnonlinear systemsneural control |
| spellingShingle | Bańka Stanisław Dworak Paweł Jaroszewski Krzysztof Design of a multivariable neural controller for control of a nonlinear MIMO plant International Journal of Applied Mathematics and Computer Science mimo multivariable control systems nonlinear systems neural control |
| title | Design of a multivariable neural controller for control of a nonlinear MIMO plant |
| title_full | Design of a multivariable neural controller for control of a nonlinear MIMO plant |
| title_fullStr | Design of a multivariable neural controller for control of a nonlinear MIMO plant |
| title_full_unstemmed | Design of a multivariable neural controller for control of a nonlinear MIMO plant |
| title_short | Design of a multivariable neural controller for control of a nonlinear MIMO plant |
| title_sort | design of a multivariable neural controller for control of a nonlinear mimo plant |
| topic | mimo multivariable control systems nonlinear systems neural control |
| url | https://doi.org/10.2478/amcs-2014-0027 |
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