Application of a flexible structure artificial neural network on a servo-hydraulic rotary actuator

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In this article the results of the application of a flexible structure artificial neural network for controlling the angular velocity of a servo-hydraulic rotary actuator are discussed. A mathematical model for the system is derived, and a flexible artificial neural network (ANN)-based controller with the feedback error learning method as a learning algorithm is applied to the system. The neural network-based controller has a feed-forward structure and three layers. The flexible bipolar sigmoid function was used as the activation function of the network. The simulation and experimental results show good performance of the developed method in learning the inverse dynamic of the system and controlling the angular velocity of the rotary hydro motor. The advantages of the developed method for servo-hydraulic actuators over other traditional approaches are discussed.


Original languageEnglish
Pages (from-to)559-569
Number of pages11
JournalThe International Journal of Advanced Manufacturing Technology
Issue number5-6
Early online date19 Oct 2007
Publication statusPublished - 1 Nov 2008


  • mathematical modeling, servo hydraulics, feedback error learning, bipolar sigmoid function, flexible neural networks