State Estimation and Fault Detection of an Electrohydrostatic Actuator

Abstract

The electrohydrostatic actuator (EHA) is an efficient type of linear actuator commonly found in aerospace applications. It consists of an external gear pump (fluid), an electric motor, a closed hydraulic circuit, a number of control valves and ports, and a linear actuator. An EHA, built for experimentation, is studied in this paper. Two types of estimation strategies, the popular Kalman filter (KF) and the smooth variable structure filter (SVSF), are applied to the EHA for kinematic state and parameter estimation. The KF strategy yields the statistical optimal solution to linear estimation problems. However, the KF becomes unstable when strict assumptions are violated. The SVSF is an estimation strategy based on sliding mode concepts, which brings an inherent amount of stability to the estimation process. Recent advances in SVSF theory include a time-varying smoothing boundary layer. This method, known as the SVSF-VBL, offers an optimal formulation of the SVSF as well as a method for detecting changes or faults in a system. In addition to the application of the KF and SVSF for state estimation, the SVSF-VBL is applied to the EHA for the purposes of fault detection. The EHA is operated under various operating conditions (normal, friction fault, leakage fault, and so on), and the experimental results are presented and discussed.