Research On Elevator Group Control Algorithm And Simulation Based On Multi-pattern

The elevator has been more and more widely used in our life as an important vehicle of modern buildings.In order to improve the running efficiency of the elevator,needing to control and schedule more than one elevators.However,the traditional elevator group control method often exhibits many problems,such as inconvenience calling,long waiting time and inefficacy power controlling.This thesis chooses building elevator group as the research object,analyzing characteristics and response mechanisms of different traffic flow pattern of elevator group,including uplink peak pattern,downlink peak pattern,2 channel traffic pattern,4 channel traffic pattern,floor balance pattern and free traffic pattern.Using fuzzy neural network to recognize features of traffic pattern,determining the network structure,and training the neural network.Evaluation function was also cleared based on average waiting time,average riding time,long waiting percent,power consume and etc.Hall response time,maximum response time,response capability,degree of aggregation and overall utilization were used as input parameters.By fuzzifying the input variables,establishing the mechanism of fuzzy and completing the elevator allocation.Finally,the transportation maker and group control algorithm model were established with MATLAB software.An experiment was conducted using new group control algorithm by taking 4 elevators as an example.The simulation results show that the improvement of multimode group control algorithm exhibits advantages in average waiting time,average riding time,long waiting percent and power consume.