Research on anti-saturation horizontal vibration model and neural network adaptive integral terminal sliding mode control strategy for high-speed elevator car system

A neural network adaptive integral terminal sliding mode control method with input saturation is proposed for the horizontal vibration problem of high-speed elevator car systems caused by uncertainties such as guideway excitation and shaft piston wind change. First, considering the input saturation problem existing in the elevator control actuator, a class of smooth functions is introduced to approximate the nonlinearity of switching saturation, and an eight-degree-of-freedom asymmetric anti-saturation nonlinear system model of the high-speed elevator car is established; second, in order to solve the singularity problem existing in the terminal sliding-mode control, a nonlinear term is introduced into the sliding-mode design, and a neural network is utilized for the fitting of the complex unknown function, and the design of the an adaptive integral terminal sliding mode controller (AITSMC), which enables the state variables of the system to achieve finite time convergence and proves the stability of the system by using Lyapunov theory; finally, under the action of two typical guide excitations, the proposed controller is compared with the passive control and adaptive control (AC), and the results show that, after adopting the proposed control method, the vibration acceleration eigenvalue is reduced by more than 60%, which effectively suppresses the horizontal vibration of the car system and verifies the effectiveness of the proposed controller.