RBF Optimization control based on PSO for elevator group system

Elevators play an important role in today urban life. The elevator group control (EGC) problem is related to many factors, such as stochastic traffic states, the number of customers, running condition, and it is difficulties in analysis, design and control. In order to increase the elevators running efficiency and quality of service, the optimizing control strategy of elevators is studied in this paper. A new elevator group system control method based on RBF and PSO is described. The RBF neural network is applied in the control strategy during the allocating landing calls to the elevators. The Particle Swarm Optimization (PSO) is used to optimize the neural-controller. Some of the connection weighted parameters of RBF neural network can be modified and optimized based on the PSO, the control performance influencing on the elevator group can be gained. The simulations are included to verify the effectiveness of the proposed method. The results prove that the method is effective.