Dynamic Force Control of Displacement Controlled Drives by Means of an Iterative Learning Algorithm

Abstract

Displacement controlled drive trains are applied more and more in industry because they are an energy efficient alternative to valve control. One major burden, for the use in a wide range of application, is the lower dynamic performance, which is caused by the large inertial forces of either swash plate pumps or speed controlled constant pumps. Additional challenges are the lack of control strategies, which determine the overall system dynamic performance. Starting with a comparison of four different linear drive trains (valve control, electro-mechanic, pump control, speed control) it is shown that the potential of a speed controlled drive train can be obtained by the use of an iterative learning controller. Once the controller is parameterized it is able to improve the tracking behavior significantly without having any effect on the plants stability.