Robust Position Control of Electro Hydrostatic Actuator with System Uncertainties Using Sliding Mode Control and Neural Networks Algorithm

This paper deals with robust position control of electro-hydrostatic actuator (EHA) systems with system uncertainties. EHA systems compared with conventional hydraulic actuator (CHA) systems are controlled by electric motor directly connected to bidirectional pump and its size is much smaller due to the component integration such as a pump, a reservoir, various valves and horses. In addition, EHA systems have fast response property because of high stiffness. Therefore, EHA systems can replace CHA systems as well as satisfy system performance. In spite of the merit of EHA systems, EHA systems in construction field are exposed to many system uncertainties due to the distinct characteristics of construction field. However, it is difficult to measure or estimate system uncertainties. Thus, sliding mode controller (SMC) and radial basis function neural networks (RBFNNs) algorithm are designed to compensate system uncertainties and to achieve robust control performance with EHA systems.