Research on neural network-based composite control strategy for hydraulically driven manipulators

Hydraulically driven manipulators face challenges in achieving high-precision tracking control due to their strong nonlinearity, parameter perturbations, and external load disturbances. In response, this article proposes a neural network-based composite control strategy. A radial basis function (RBF) neural network is employed to approximate and compensate for time-varying disturbances. The state-space equations are reformulated to incorporate parameter perturbations as an extended state variable. An Extended State Observer (ESO) is integrated to estimate parameter perturbations for feedforward compensation and observe the system state variables. Subsequently, a composite controller is designed by integrating backstepping and sliding mode control, with the stability of the closed-loop system proven via Lyapunov theory. Finally, simulation results demonstrate that the proposed control strategy exhibits higher control accuracy and superior robustness compared with PID control and traditional neural network-based nonlinear control.