The Elevator Group Control System Based On Fuzzy Control

With the vigorous development of social economy and the modern construction industry and the accelerating of urbanization construction, the modern urban space is insufficient, vertical transportation problem becomes more and more prominent. So single elevator doesn’t meet the needs of all passengers, as a result, many sets of elevator design side by side becomes another development trends. Elevator group control is actually the optimal problem of multiple elevators, it can make the whole transport system of a certain or some performance index to get the optimal. Its complexity is mainly manifested in the multi-objective, uncertainty of the elevator group control system, and the incompleteness, nonlinear, disturbance of the information. At the same time, the establishment of elevator group control simulation platform is also complex.This paper introduces the background and the research status of the elevator group control system firstly, and then introduces the characteristics of the elevator group control system and artificial intelligence technology in the application of the elevator group control system, at the same time analysis the fuzzy control technology in the necessity and feasibility of the application of the elevator group control system. Finally, introduces the effect of elevator traffic patterns on improving the performance of the elevator system, summarizes the main factors influencing the elevator features. Using fuzzy logic control module in the Lab VIEW to design the fuzzy controller, including the new input variables, calculation, blurred, the realization of a fuzzy rules and fuzzy reasoning, etc. Then established reasoning rule base ensuring the implementation of the fuzzy scheduling algorithm, finally with software design of the virtual simulation system of the elevator. The system adopts the modular design idea, design the elevator hall layer sharing module, capsules module and the fuzzy logic scheduling algorithm module. The simulation results illustrated that the more correctness and effectiveness of fuzzy control compared with the traditional control.