Research Of Intelligent Elevator Group Control System

There appears a greater demand on the function of elevator with more and more high-story on service in China. People ask for the greatest index from the elevator system with the minimal average waiting time, the minimal single waiting time, energy-saving and the average greatest transportation. It's clear that the single control of elevator can't meet the demands mentioned above. This task, which takes the elevator group as a complete system with the aid of intelligent control technology such as neutral network, fuzzy control, gene algorithm to realize the elevator group control, aims at best result of these index.The passengers'psychological and physical requirement put forward various demands on elevator group control system. To meet these demands, the newly-designed elevator group control system should be a complex system containing such features as multi-objective, uncertainty, nonlinear and information inaccuracy. Aiming at such system feature, this task puts forward the idea of forecasting control model, optimizing control mode, decomposition-coordination control model and fuzzy control.With the aid of the fuzzy neural network, this task designs to identify the different passenger flow, and classify it into the six models as the up-peak service model, down-peak service, two way traffic model, four way traffic model, the balanced bi-story traffic model and free duty traffic model. It also put forward the design of five-level fuzzy neural network and apply the classification into the elevator group control and raises the best group control strategy to each model.Because there appears the problem of the poor switch among each model, the task raises the elevator group control strategy based on the fuzzy neural network to realize the smooth swift among each model and optimize the multi-objective under the various models with the determined index of Gene algorithm.Finally, with the introduction of the CAN bus and DSP technology, it applies the elevator group control strategy into the engineering.