ABSTRACT
Electro-Hydraulic Servo Systems (EHSS) are employed as actuators to track the desired trajectory and exert force in heavy-duty industrial applications. The EHSS is often prone to problems such as leakage and actuator seal damage during the course of its utilization. These faults which cannot be directly detected from current sensor values, can eventually result in complications and degrade control performance. The goal of this research is to use representation learning concepts to detect these faults with decreased complexity. The objective is to find a nonlinear mapping to transform raw data into another space in which classification becomes easier. The data are driven from the hydraulic supply pressure signal. To find the mapping, a custom-built optimization algorithm is proposed along with a suitable cost function to carry out the search for the new representation. The performance of the resulting transformation is tested in an experimental setting to show the merits of the proposed method.
ABSTRACT
The electro-hydraulic servo system (EHSS) demonstrates a relatively low level of efficiency compared to other available actuation methods. The objective of this paper is to increase this efficiency by introducing a variable supply pressure into the system and controlling this pressure during the task of position tracking. For this purpose, an EHSS structure with controllable supply pressure is proposed and its dynamic model is derived from the basic laws of physics. A switching control structure is then proposed to control both the supply pressure and the cylinder position at the same time, in a way that reduces the overall energy consumption of the system. The stability of the proposed switching control system is guaranteed by proof, and its performance is verified by experimental testing.
