Neural adaptive braking control for single rope winding hoisting systems with disturbance compensation

In the braking control of single rope winding hoisting systems (SRWHS), reasonable braking torque is of great significance in reducing the probability of significant hidden dangers such as overwinding, overspeeding, rope breaking, vibration, conveyance crash and so on. However, dynamic nonlinearity, time-varying disturbances, mechanical dynamic uncertainties and measurement noise severely affect the practical braking control performance. To address those problems, a neural adaptive braking torque controller with disturbance compensation is developed to enhance the braking performance for a single rope winding hoisting system in this paper. Firstly, considering the elastic of steel wire rope, the nonlinear braking model of the SRWHS is established using Lagrange equations and an extended state observer (ESO) is introduced to estimate the system’s unmeasured states and modelling error. Next, a neural adaptive controller is developed to estimate and compensate the mechanical dynamical disturbances resulted from friction forces and random external disturbances. Then, a neural adaptive network controller combined with the ESO (ESONAC) is designed to solve the modelling nonlinearity, time-varying disturbances and system’s unmeasurable states. Finally, the advantages of the ESONAC in improving the braking control performance of winding hoisting systems is verified by comparative experiments.