Disturbance compensation based robust backstepping control for 2-DOF electro-hydraulic tunneling robot

In order to suppress the influence of uncertain disturbances on the trajectory tracking of hydraulic manipulator, a composite control strategy for the cutting electro-hydraulic driving system (CEHDS) of the tunneling robot is presented, which synthesizes the advantages of neural networks technique, recursive backstepping and adaptive control theory. The Lagrangian model with actuator dynamics is derived based on the practical tunneling robot. The back-stepping method is utilized for the strictly feedback state-space model. To address the matched and unmatched lumped uncertainties, the radial-basis-function neural networks (RBFNNs) are employed to approximate the unmatched term which contains the nonlinear friction torque and external cutting load in the mechanical subsystem. The nonlinear disturbance observer (NDOB) is utilized to estimate the matched lumped uncertainty in the hydraulic subsystem. Simultaneously, the adaptive robust mechanism is proposed to compensate the residual disturbances. Based on the Lyapunov theorem, the stability and the bounded tracking error of the CEHDS are obtained. The simulation and experimental results validate the effectiveness of the proposed method in comparison with the common backstepping and PID-controller approaches.