Aiming at the problems existing in the internal detection of Gas-Insulated Switchgear (GIS) equipment in substation, such as the interference of cleaning reaction force in positioning, the difficulty in balancing the detection efficiency and cleaning effect under the constraint of battery capacity, and the response of millisecond electric field change, this paper proposes a hierarchical cooperative control model of power and cleaning system. The upper planning layer adopts model predictive control (MPC) combined with digital twinning technology to preview the global path, and constructs a multi-objective optimization framework including robot dynamics, cleaning reaction force, coverage dynamics and battery constraints. Sliding mode controller (SMC) is designed in the lower executive layer to distribute driving and cleaning power in real time, and the distribution coefficient is optimized online through reinforcement learning to dynamically balance energy consumption and performance. The conflict resolution mechanism introduces the vibration safety priority strategy, and when the vibration of the fuselage exceeds the limit, the cleaning power is reduced in milliseconds to ensure the positioning accuracy. The simulation results show that the model can reduce the path tracking error by more than 60%, achieve the cleaning coverage of 94.8% under the battery constraint, improve the comprehensive energy efficiency ratio by about 20%, and the vibration response can be restored to the safety threshold within 3ms, which is significantly superior to the traditional PID sequential control and uncompensated MPC method, and provides an effective collaborative control solution for the engineering application of GIS internal inspection robots.
Zhu et al. (Sun,) studied this question.