Steering Control of Automated Guided Vehicle by Neural Network

Abstract

Factory automation is advanced in industrial field, and then higher speed drive and performance are desired for an AGV (automated guided vehicle). A new steering control of AGV by fuzzy control has been proposed instead of the PI control. However, it is necessary much time to investigate the rules and to adjust the scaling factors for excellent performance in the fuzzy control. Thus, this is the matter to be solved.
In this paper, a new steering control of AGV based on the neural network using the back propargation method is proposed. The good steering control results by the fuzzy control is adopted for the teaching signal of neural network. At the first step, the effect of the number of learning and the learning errors on the steering control results are discussed by the computer simulation using the AGV model. Further, the ability of generalization in the turning radius and the traveling speed is also investigated. It becomes clear that the AGV can travel along the traveling course provided that the neural network learns both of the right and left turning at the maximum traveling speed and the minimum turning radius. Then, it is proved by the experiment using the AGV built as a trial that the proposed steering control method is very available.