Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network

Alexander Hošovský, Ján Piteľ and Kamil Židek

Abstract and Applied Analysis, 2015, vol. 2015, 1-16

Abstract:

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaaa:906126

DOI: 10.1155/2015/906126

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