Elevator Traffic Analysis And Research On Elevator Group Control Strateges

With the development of economy and the advance of science and technology, elevators as the main vertical transportation tool in the buildings have been an indispensable part in our daily life. As the size of building enlarging and the number of installed elevators increasing, elevator traffic flow takes on new changes and then the high quality of elevator group control system (EGCS) is required. Hence, on the one hand, elevator traffic flow need to be surveyed to analyze the pattern, on the other hand, it needs studying the group control strategy to handle the elevator traffic flow. The dissertation investigates some key technology of EGCS, and the main contribution is as follows:The theory and method of elevator traffic flow generation is a key problem to further study the elevator group control strategies. In order to describe the passenger distribution, the concept of the elevator passenger origin-destination (O-D) matrix is first put forward. Based on t elevator traffic data collected, in terms of the theory of thermodynamics entropy and information entropy, the maximizing entropy model is constructed to estimate the elevator passenger O-D matrix. The model is solved using Lagrange multiplier method and heuristic genetic algorithm respectively. The computational example shows that GA is significantly superior to the Lagrange multiplier method. Based on the passenger O-D matrix estimated, the Mento Carlo sampling method is utilized to simulation traffic flow. With the traffic measure executed continuously, the O-D flow is generated using the model and set up the O-D database. Elevator group simulation test is executed based on the O-D database.Elevator traffic prediction is the basis of elevator traffic pattern recognition and the important component of EGCS. To this question, wavelet support vector machines (WSVM) is presented to construct elevator traffic flow prediction model by combining the historical data and recent passenger data. The prediction model is trained by SMO algorithm. The simulation showed that the prediction result not only retains the tendency of historical data but also tracking the passenger flow change in time. Through the comparison of prediction results by BP neural network, ARMA and GSVM methods, it is showed that the WSVM can increase the prediction accuracy and preciseness.Elevator traffic pattern recognition is the important functional module. A new method of elevator traffic pattern recognition based on Gaussian kernel particle swarm optimization K-means clustering algorithm (GKPSOKCA) is proposed. In order to enhance the robustness of the clustering algorithm, fitness function is devised by Gaussian kernel metric replacing the Euclidean distance. The robustness of the clustering algorithm is analyzed using the influence function of M-estimator. Simulation shows that the GKPSOKCA can classify elevator traffic patterns precisely without any prior knowledge. Compared with IEKCA, GKPSOKCA needs less parameters to determine, can be easily implemented, and has stable convergence characteristic with good computational efficiency. Elevator traffic pattern recognition with GKPSOKCA can be used as a module in EGCS and assist EGCS in making decisions in order that EGCS improves the service performance under different traffic situations.Reinforcement learning system based on prior knowledge applying to elevator group control dispatching is significantly enhancing the convergent speed of reinforcement learning. The dissertation uses the CMAC network to build the elevator group reinforcement learning system. On the one hand, addition of prior knowledge can reduce the state space explored by reinforcement learning algorithm, and enhance the convergent speed of reinforcement learning. On the other hand, CMAC network has the good online incremental learning capability and good convergent speed, and hasn't local minima. From the two sides, the elevator group dispatching is optimized. The simulation result verified its effectiveness.Lastly, the elevator group control simulation environment is designed, including elevator group control test-bed and elevator group control simulation system. Elevator group control test-bed conforms to the real EGCS structure and can satisfy the need for study on the EGCS. Elevator group control simulation system provides the simulation platform for the study on group control strategy. Based on the simulation platform, simulation study of group control strategy is executed and the results showed that reasonable dispatching is implemented among different traffic flow pattern based on traffic pattern recognition.