Adaptive boundary control for a friction hoisting system during the loading process

This paper aims at the rope dynamic tension fluctuation (DTF) control in the friction hoisting system (FHS) during the loading process. The exact dynamic modelling of the tail and head ropes connected to the cage is considered. A disturbance observer is proposed to track the actual impact disturbance. The actuator acts at the friction wheel to pretension the system. The use of an adaptive neural network allows for the handling of system uncertainty. An appropriate control law is developed to stabilize the cage and suppress the rope DTF in FHS during the loading process. Numerical simulations and theoretical analysis are presented to prove the feasibility and effectiveness of the proposed control law. Numerical analysis results show that the head rope dynamic displacement fluctuation (DDF) and DTF amplitudes can be suppressed to at least 1/125 of their initial fluctuation amplitudes under the proposed control law.