Research On Optimization Algorithm Of Elevator Group Control System

As an indispensable transportation equipment in modern buildings,elevators are widely used in residential buildings,hospitals,shopping malls,office buildings and other buildings.Among the traditional scheduling methods,the most common one is the minimum wait time scheduling algorithm.It takes the waiting time of passengers as the control target and fails to consider other issues such as optimizing the trip of elevators and reducing energy consumption.With the rapid development of intelligent technology,more and more researchers put forward a multi-objective optimal scheduling system for the intelligent control of elevator group control system from the perspective of passengers and managers.The structure of elevator group control system is complex.In order to achieve better control effect of the optimal scheduling of the group control system,the characteristics of randomness,disturbance,multi-objective and nonlinear are analyzed and studied,and the algorithm for improving the service performance of the group control system is designed,and simulation and verification is carried out.The research on elevator group control system is mainly carried out from the following three parts.(1)Based on the minimum waiting time algorithm,the shortest distance scheduling algorithm is used to design a group control scheduling system for multiple elevators.The elevator simulation platform with PLC as the controller and the simulation object model are used to verify the algorithm.The simulation data are obtained,and the simulation results of application distance scheduling are summarized and analyzed.(2)According to the passenger flow characteristics of the elevator group control system and the passenger flow running pattern,the BP neural network is selected to identify the running pattern.The genetic algorithm is introduced to improve and optimize the network in order to make the network converge quickly,reduce the recognition error,improve the prediction accuracy of the network and the intelligent recognition degree of the elevator group control system.(3)Based on passenger flow pattern recognition,the particle swarm optimization(PSO)is to improve the multi-objective optimization scheduling of the elevator group control system.Taking the waiting time,the running time and the energy consumption of system as the optimization objectives and establishing a comprehensive evaluation function,then the modular design of the whole simulation system is carried out.The simulation experiments of different running patterns are carried out in MATLAB.The data are compared and the simulation results are analyzed.It is proved that this method has better control effect in group control scheduling.There are 22 images,16 tables and 81 references in this paper.