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Neural network control for automatic guided vehicles using discrete reference markers

The conventional control of automatic guided vehicles (AGV) which use discrete reference markers includes a three-term PID controller to control the operation and the motors of the vehicle. The parameters of the PID controller were given whenever the vehicle was to be operated. The achievement of the best performance with respect to the chosen PID parameters was a matter of trial and error. In this paper, a neural network controller is proposed as an indirect-controller to obtain the best control parameters for the main controller in use with respect to the location (position) of the AGV. This neural network controller (NNC) was trained using supervised learning along with a backpropagation algorithm. Also in this paper a brief introduction summary of the control of the AGV, experimental results of the indirect NNC are presented. Finally a comparison between the conventional controller and the proposed NNC with its advantages is presented.