Abstract
This study utilizes the direct neural control (DNC) based on back propagation neural networks (BPN) with specialized learning architecture applied to control the position of a cylinder rod in an electro-hydraulic servo system (EHSS). The proposed neural controls without the specified reference model use a tangent hyperbolic function as the activation function, and the back propagation error is approximated by a linear combination of error and error’s differential. The hydraulic cylinder subjected to varied load is also proposed. The simulation and experiment results reveal that the proposed neural controller is available to position control with high convergent speed, and enhances the adaptability and stability in varied load condition.
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© 2008 Springer-Verlag Berlin Heidelberg
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Kang, Y., Chen, YW., Chang, YP., Chu, MH. (2008). The Direct Neural Control Applied to the Position Control in Hydraulic Servo System. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_21
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DOI: https://doi.org/10.1007/978-3-540-87734-9_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87733-2
Online ISBN: 978-3-540-87734-9
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