Predefined-Time Prescribed Performance Neural Network Control for Asymmetric Hydraulic Cylinder Systems

This paper investigates a class of electro-hydraulic servo systems with unknown nonlinear functions and parameters. To address the issues of modeling uncertainties and unmodeled dynamics, an adaptive robust nonlinear controller integrating neural networks and predefined-time prescribed performance is proposed. First, an exponential-type predefined-time prescribed performance function is designed to ensure that the system tracking error converges to a prescribed region within a predefined time. An adaptive law based on the discontinuous projection method is developed to estimate unknown parameters and compensate for them in the controller. The dynamic surface technique is introduced to overcome the “explosion of complexity” problem inherent in the traditional backstepping method. Meanwhile, neural networks are employed to approximate system nonlinearities, thereby reducing modeling errors. Finally, the stability of the closed-loop system is rigorously proved using Lyapunov theory, and numerical simulations validate the superiority of the designed controller over conventional control strategies.