Physical coupling, nonlinearity and uncertainty degrade the dynamic performance of electro-hydraulic servo systems, particularly under conditions involving input delays, leading to reduced trajectory tracking accuracy or even system instability. These factors often fail to meet the high-precision requirements of engineering applications. To effectively address these difficulties, this paper proposes a novel adaptive control protocol for networked electro-hydraulic servo systems. For the minimal communication delay problem of networked electro-hydraulic servo systems, Laplace transform algorithm together with Padé approximation is adopted in this study to remove the delay term from the mathematical system model. Moreover, the matched modeling parametric uncertainty of systems is estimated and compensated by the neural network adaptive method to improve the dynamical performance of the system during the steady state. The controller is designed on the basis of recursive backstepping strategy and the finite-time stability theorem, which can handle system nonlinearity and guarantee transient response. The validity of the proposed theoretical results is proved by Lyapunov stability and the feasibility and superiority are verified via physical simulation.

In research on electro-hydraulic servo systems, nonlinearity deeply affects dynamic performance, such as the output of hydraulic actuators and the generation of control signals, leading to response hysteresis and control complexity. Moreover, during the control process, changes in the external environment and component loss lead to model parameter distort, which reduces control capability. To address these challenges, this paper conducts a structural transformation on the traditional dynamic surface controller in combination with the fast finite-time stability theorem and proposes a novel finite-time dynamic surface control strategy, which can not only overcome the differential explosion phenomenon in the recursive backstepping iterative process but also enhance the transient dynamic response speed. Furthermore, the neural network adaptive algorithm is adopted to handle the negative dynamic effect caused by parametric uncertainty. The theoretical results are verified by the Lyapunov stability method and numerical simulation.