Variable Universe Fuzzy Control of High-Speed Elevator Horizontal Vibration Based on Firefly Algorithm and Backpropagation Fuzzy Neural Network

To effectively suppress the horizontal vibration of a high-speed elevator car caused by uncertainties such as the irregularity of guide rails and the piston wind in the hoistway, this paper proposes a new variable universe fuzzy control method for semi-active guide shoes with magnetorheological (MR) dampers based on the firefly algorithm and backpropagation (FA-BP) algorithm fuzzy neural network (FNN). First, the dynamic model of a car system is constructed, and the FNN system based non-parametric model of the MR damper is established with the original MR damper data collected from experiments. Then, a variable universe fuzzy main controller is presented to control the input current of the MR damper. As for its variable universe contraction-expansion factors, another FNN auxiliary controller is designed to accurately adjust the universe. Furthermore, the FA-BP algorithm is utilized to train the FNN auxiliary controller. Finally, the elevator experiment is carried out to analyze the characteristics of horizontal vibration, and simulation tests are conducted using the measured excitation signal and two kinds of simulated excitation signals, respectively. The results show that the proposed controller has a lower horizontal vibration acceleration, tilt angle acceleration and other index values than the passive controller or the FNN controller, indicating that the proposed control method can effectively suppress the horizontal vibration of a high-speed elevator. This study can provide the technical foundation for intelligent vibration control of high-speed elevators.