Fast Finite-Time Position Tracking Control of Electro-Hydraulic Servo Systems with Parametric Uncertainty via Dynamic Surface and Neural Adaptive Method

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.