Twin Tracking Method of Elevator Tractor Condition Based on Neural Network

This work is aimed at the high time-varying, nonlinear and strong coupling problems of elevator tractor condition control. This paper proposes a nonlinear condition twin tracking algorithm based on neural network to monitor its condition and explore the establishment of real-time tracking traction machine condition visualization twin model. The neural network adaptive controller is designed by using the second-order kinematic derivative function to track and approximate the working condition trajectory of the traction machine. The robustness of the system is demonstrated by the integral Lyapunov method. The simulation results show that this method can effectively reduce the tracking error of the system, quickly approach the trajectory of the traction machine, and improve the monitoring accuracy of the industrial control system.