Electro-hydraulic position servo systems are widely applied in aerospace, industrial automation, and robotics due to their high power density, rapid response, and precision. However, inherent nonlinearities, parametric uncertainties, and external disturbances pose significant challenges to accurate control. This paper proposes a robust extended Kalman filter (EKF)-based rolling horizon H-infinity control strategy for output feedback control of such systems. First, a state-space model of the electro-hydraulic position servo system is established, integrating unknown dynamics into a single disturbance term. Second, a robust EKF with adaptive structure is designed to dynamically adjust the covariance matrix by integrating motion model observations and sensor measurements, achieving accurate state and disturbance estimation with convergence proven through rigorous stability analysis. Then, based on locally linearized system models, the control problem is transformed into a linear matrix inequality (LMI) problem, reducing real-time computational complexity. Through comparative simulations and experiments, the proposed control method demonstrates superior performance over traditional PID, backstepping, and standard MPC controllers in position tracking accuracy, disturbance rejection capability, and control smoothness. Specifically, under measurement noise and external disturbances, the proposed method achieves smaller mean square tracking error (0.022) and maximum error (0.055) while maintaining acceptable computation time (27.3 ms). The research findings provide an effective solution for high-performance control of electro-hydraulic position servo systems and offer valuable reference for improving control performance of similar nonlinear uncertain systems.
Wang et al. (Sat,) studied this question.