Abstract: To address the issues of low efficiency and high risk associated with the manual docking of mobile DC ice-melting devices with transmission lines, this paper designs an automated docking system based on a lifting manipulator. To overcome parametric uncertainties and external disturbances in the system, a composite strategy integrating Radial Basis Function (RBF) neural networks and adaptive sliding mode control is proposed. The strategy utilizes the neural network for online disturbance compensation, and system stability is demonstrated based on Lyapunov theory. Simulation results show that, in trajectory tracking and step response tests, the proposed method outperforms traditional control approaches in terms of control accuracy, response speed, and steady-state performance, effectively enhancing the reliability and automation level of the docking process.