A hybrid control for elevator group system

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 hybrid control method which optimizes passenger service in an elevator group is described. It is capable of optimizing the neural-controller based on Particle Swarm Optimization (PSO) of an elevator group controller. Starting from the operation characteristics of elevator group control system, the architecture and the traffic pattern of an elevator group control system are described, and the optimization cost criterion function is proposed. The PSO algorithm is used to optimize the weights and biases of the neural network. Some weighted parameters of the Radial Basis Function (RBF) neural network can be modified based on the PSO, so that different weight settings and their influence on the elevator supervisory group control (ESGC) performance can be tested. It can reduce the passenger's average waiting time by allocating an appropriate number of elevator cars to the lobby floor. The results prove that the hybrid method is effective.