Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network
To make effective use of model-based control system design techniques, one needs a good model which captures system’s dynamic properties in the range of interest. Here an analytical model of pneumatic muscle actuator with two pneumatic artificial muscles driving a rotational joint is developed. Use...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2015/906126 |
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| author | Alexander Hošovský Ján Piteľ Kamil Židek |
| author_facet | Alexander Hošovský Ján Piteľ Kamil Židek |
| author_sort | Alexander Hošovský |
| collection | DOAJ |
| description | To make effective use of model-based control system design techniques, one needs a good model which captures system’s dynamic properties in the range of interest. Here an analytical model of pneumatic muscle actuator with two pneumatic artificial muscles driving a rotational joint is developed. Use of analytical model makes it possible to retain the physical interpretation of the model and the model is validated using open-loop responses. Since it was considered important to design a robust controller based on this model, the effect of changed moment of inertia (as a representation of uncertain parameter) was taken into account and compared with nominal case. To improve the accuracy of the model, these effects are treated as a disturbance modeled using the recurrent (Elman) neural network. Recurrent neural network was preferred over feedforward type due to its better long-term prediction capabilities well suited for simulation use of the model. The results confirm that this method improves the model performance (tested for five of the measured variables: joint angle, muscle pressures, and muscle forces) while retaining its physical interpretation. |
| format | Article |
| id | doaj-art-9572739208c747a68b7c708b72887d70 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-9572739208c747a68b7c708b72887d702025-02-03T05:46:16ZengWileyAbstract and Applied Analysis1085-33751687-04092015-01-01201510.1155/2015/906126906126Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural NetworkAlexander Hošovský0Ján Piteľ1Kamil Židek2Faculty of Manufacturing Technologies with a Seat in Prešov, Technical University of Košice, Bayerova 1, 080 01 Prešov, SlovakiaFaculty of Manufacturing Technologies with a Seat in Prešov, Technical University of Košice, Bayerova 1, 080 01 Prešov, SlovakiaFaculty of Manufacturing Technologies with a Seat in Prešov, Technical University of Košice, Bayerova 1, 080 01 Prešov, SlovakiaTo make effective use of model-based control system design techniques, one needs a good model which captures system’s dynamic properties in the range of interest. Here an analytical model of pneumatic muscle actuator with two pneumatic artificial muscles driving a rotational joint is developed. Use of analytical model makes it possible to retain the physical interpretation of the model and the model is validated using open-loop responses. Since it was considered important to design a robust controller based on this model, the effect of changed moment of inertia (as a representation of uncertain parameter) was taken into account and compared with nominal case. To improve the accuracy of the model, these effects are treated as a disturbance modeled using the recurrent (Elman) neural network. Recurrent neural network was preferred over feedforward type due to its better long-term prediction capabilities well suited for simulation use of the model. The results confirm that this method improves the model performance (tested for five of the measured variables: joint angle, muscle pressures, and muscle forces) while retaining its physical interpretation.http://dx.doi.org/10.1155/2015/906126 |
| spellingShingle | Alexander Hošovský Ján Piteľ Kamil Židek Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network Abstract and Applied Analysis |
| title | Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network |
| title_full | Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network |
| title_fullStr | Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network |
| title_full_unstemmed | Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network |
| title_short | Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network |
| title_sort | enhanced dynamic model of pneumatic muscle actuator with elman neural network |
| url | http://dx.doi.org/10.1155/2015/906126 |
| work_keys_str_mv | AT alexanderhosovsky enhanceddynamicmodelofpneumaticmuscleactuatorwithelmanneuralnetwork AT janpitel enhanceddynamicmodelofpneumaticmuscleactuatorwithelmanneuralnetwork AT kamilzidek enhanceddynamicmodelofpneumaticmuscleactuatorwithelmanneuralnetwork |