Neuromorphic controller for AGV steering

Author

Cheng, R.M.H. ; Xiao, J.W. ; Lequoc, S.

Author_Institution

Dept. of Mech. Eng., Concordia Univ., Montreal, Que., Canada

fYear

1992

fDate

12-14 May 1992

Firstpage

2057

Abstract

A backpropagation neural network is proposed as a controller for an automated guided vehicle (AGV) system. At the present stage of development, the input layer consists of two neurons and receives the state signals of the tracking errors from the camera image processor, and the sole neuron in the output layer provides the command signal of a reference yaw rate signal for the vehicle. Simulations and preliminary experimentation on a prototype vehicle showed that one hidden layer was adequate to provide good driving for such a time-varying nonlinear dynamic system. A comparison with a previous proportional controller is included

Keywords

automatic guided vehicles; backpropagation; neural nets; nonlinear control systems; position control; time-varying systems; AGV steering; automated guided vehicle; backpropagation neural network; camera image processor; neuromorphic controller; reference yaw rate signal; time-varying nonlinear dynamic system; tracking errors; Automatic control; Backpropagation; Cameras; Control systems; Neural networks; Neuromorphics; Neurons; Signal processing; Vehicles; Virtual prototyping;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 1992. Proceedings., 1992 IEEE International Conference on

Conference_Location

Nice

Print_ISBN

0-8186-2720-4

Type

conf

DOI

10.1109/ROBOT.1992.219978

Filename

219978