Research On Intelligent Optimal Scheduling Strategy For Elevator Group Control System

The trend in constructing high-rise buildings in the world today has resulted in a demand for better transportation services among floors in the buildings. As elevator is primary transportation facility in buildings, the system that controls it must maximize its transportation capability and improve the quality of passenger service. In modern buildings of residential, commercial or institutional types, the control system must be able to execute command on a group of elevator cars in an effective and efficient manner possible.The thesis analyses the systematic characteristic, control method, performance evaluation index and traffic flow situation of EGCS at first. Then sets up flow of passengers and analysing models. This model can produce the flow of passengers data under any traffic mode, offering the emulation environment for EGCS. In traffic pattern-recognition, fuzzy neural network is adopted to distinguish the traffic flow, this method has very good recognition capability to the complicated traffic flow and can play very good guidance function to adopt the corresponding group control policy to EGCS according to different traffic.In this paper, the adaptive fuzzy logic controller for elevator control systems is a self-tuning type since the controller is adjusted by modifying the membership functions and selecting the most appropriate fuzzy rule set based on the measured performance results. By exploiting the measured performance information, i.e. the waiting time data (AWT), satisfactory performance of the controller can be achieved and maintained since the outcomes of the controlled system are always used to initiate corrections in the control actions through suitable modifications of the membership functions and proper selection of the fuzzy rule set. The simulation results showed considerable overall improvements in the performance criteria evaluated as compared to the performance of conventional group controllers.