Analysis Of Elevator Traffic And Research On Optimization Control Method Of Elevator

With the development of the society, there are more and more high buildings. Elevator has become one of the necessary mechanical and electrical equipments in the life and industry. How to make the elevators play the parts better has been concerned. The study on elevator control is one of the hot research subjects. In current, there are fewer studies on the analysis of the characteristic for elevator traffic flow. However, as the basic problem in elevator group control, the characteristic study for elevator traffic flow is necessary. In addition, the elevator group control algorithm, which is with integrated theory basis, simple and effective, and easily realized, is an important content in the elevator group control scheduling. A well elevator speed control method is an effective way to improve the elevator control level, while the present method is mainly on the basis of the traditional control, the application of the intelligent control method is relatively less.The elevator analysis is an important part of the elevator control, and which is studied firstly in the paper. On the basis of the elevator analysis, the optimal control of elevator group is studied; on the other hand, with the purpose to improve the running performance of the elevator, the zero speed parking control of elevator is studied. Due to the nonlinear of elevator traffic, the nonlinear and multi-objective of elevator control, intelligent control theory is used to improve the service level of the elevator as well as the running quality of the elevator.The prediction of elevator traffic flow is one of the basis problems in elevator group control. Combining with the nonlinear and small sample characteristic, support vector machine (SVM) is used to predict the elevator traffic flow time series. The takens'delay coordinate phase reconstruction of chaotic dynamical system is introduced, and the input dimension of the time series is ascertained. The small data set method is applied to analyze the chaotic property of elevator traffic flow time series, which is validated by Poincare section method. Then the prediction model of elevator traffic flow chaotic time series based on SVM is established, and the optimal model parameters are obtained by experiment. So the incoming and outgoing passenger flow time series are predicted respectively using the data collected in some building. Meanwhile, comparing research with RBF neural network model, traditional moving smoothing method and exponential smoothing method are given. Simulation results show that the trend of the factual traffic flow is better followed by traffic flow obtained by the proposed method. The fitting and prediction of elevator traffic flow with better effect can be realized. In the elevator group control system, when the elevator group is scheduled by suitable algorithm according to traffic mode, the performance of elevator group control system will be improved. The kernel fuzzy clustering (KFCM) algorithm based on particle swarm optimization (PSO) is proposed to realize the elevator traffic mode identification. The iterative process based on gradient descent in KFCM algorithm is replaced by PSO, which has stronger global and local search capability. Meanwhile the sensitivity to initial value of FCM is decreased. By using kernel method, the sample in the low-dimensional feature space is mapped into high-dimensional feature space. And the sample feature is optimized and can be linearly divided in high-dimensional feature space so that clustering could be performed efficiently. The elevator traffic flow data collected is regard as the test sample. The simulation results show that the algorithm proposed has better performance indices, and the clustering effect of traffic flow is more exact.Elevator group control scheduling algorithm is the core of elevator group control system, the research on which has important meaning to improve the running efficiency and holistic performance of elevator. On the basis of the traditional algorithm, based upon the uncertainty and the multi-target, two scheduling algorithms based on fuzzy control are proposed, including fuzzy area control scheduling algorithm with multi-objective and fuzzy multi-rule scheduling algorithm with multi-objective. The realization of algorithm is described combined with objective function, and the application range of the algorithm is discussed. The performances of the scheduling algorithm are validated by simulation.Zero speed parking problem of elevator is another basic problem in elevator control. The prediction control is applied in zero speed parking of elevator. The creeping-in distance predicted is added to the uniform motion stage to decrease or eliminate the distance, so the zero speed parking of elevator is realized. The optimal principle of elevator speed curve is given, and the prediction models based on wavelet neural network (WNN), RBF neural network and BP neural network are established respectively. The genetic algorithm is used to optimize the parameters to improve the prediction precision of WNN. Comparing research of the prediction performance among the three models is carried through by simulation. Meanwhile, the elevator running performance after using zero speed parking algorithm is obtained by simulation, and the effect of the proposed method is validated.