İlyas İSTİF, Kenan KUTLU

Oransal valf kontrollu hidrolik silindir sisteminin konum kontrolu

Bu çalışmada simulink modeli geliştirilen oransal valf kontrollü hidrolik silindir sistemi için konum kontrolü gerçekleştirilmiştir. Bu sistemler endüstride robotlarda, takım tezgahlarında, havacılıkta uçuş ve yerçekimi simülatörlerinde ve savunma sanayinde yaygın olarak kullanılmaktadır. Bond graph modeli de verilen 4. mertebeden nonlineer sisteme, yapay sinir ağı (YSA) model temelli öngörülü kontrol uygulanarak, hidrolik silindirin konum kontrolü gerçekleştirilmiştir. Hidrolik sistem dinamiği üzerinde etkili olan piston yükü kütlesi ve hidrolik akışkanın eşdeğer hacimsel elastiktik modülü değiştirilerek, YSA model temelli öngörülii kontrolörün performansı da incelenmiştir. YSA model temelli öngörülü kontrolör, değişen sistem parametrelerine rağmen konum kontrolünü başarı ile gerçekleştirmiştir.

Position control of a proportional valve controlled hydraulic cylinder system

The control of electrohydraulic systems has been the focus of powerful research over the last decades. Inherent nonlinear behaviour of the hydraulic systems makes them ideal subjects for applying different types of sophisticated controllers. In this work, a simulink model is developed of a proportional valve controlled hydraulic cylinder system. This study considers the application of neural networks to the control of a hydraulic cylinder which is driven by a proportional valve, subjected to variable reference trajectory and variable load conditions. In industrial applications, these types of systems are used widely in robotics, milling machines, in aerotics field with G and motion simulators and military applications. The system model is fully implemented in Matlab's Simulink simulation package and model of each hydraulic component is developed and combined in a library for easy reuse. Bond graph model of the system is also developed. Applying neural network model predictive control (NNMPC) to the fourth order nonlinear system model, position control is performed. Performance of the NNMPC algorithm is also investigated by changing system parameters. Piston load and effective bulk modulus of hydraulic fluid are the most important system parameters which have valuable effects on the hydraulic system dynamics. Simulations show that, neural network model predictive control is successful for position control in spite of different values of piston load and different bulk modulus.

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