Electrohydraulic systems (EHSs), despite their high power and force density, pose significant challenges for accurate and robust state estimation due to inherent nonlinearities, friction effects, fluid compressibility, and various uncertainties. In this paper, two reduced-order sliding mode observer (SMO) structures are developed, including a first-order sliding mode observer and a high-order sliding mode observer based on the Super-Twisting Algorithm (STA). Within this framework, a Lyapunov-based stability analysis is established for the reduced-order configurations, and sufficient analytical conditions for selecting the observer gains are derived. Furthermore, the high-order sliding mode observer is reformulated into a reduced-order structure, and its stability is rigorously analyzed according to the resulting estimation error dynamics.

The adoption of reduced-order structures leads to decreased computational burden and faster state reconstruction compared to their full-order counterparts. To evaluate performance, the proposed observers are implemented on a five-state electrohydraulic system model under measurement noise, external disturbances, and parametric uncertainties. Their performance is compared with that of an Extended Kalman Filter (EKF) and a derivative-based observer employing a low-pass filter. Simulation results demonstrate that the reduced-order Super-Twisting sliding mode observer achieves superior estimation accuracy, faster convergence, and enhanced robustness against noise and disturbances relative to the other observers considered.