A recurrent neural network-based sequential controller for manufacturing automated systems

摘要

The objective of this paper is to propose a recurrent neural network (RNN)-based sequential controller to be used in an automated manufacturing system. Contrary to the programmable controller, an RNN-based sequential controller is based on a definite mathematical model rather than a trial and error technique. The proposed controller is also more flexible since it is not limited by the restrictions of finite state automata theory. A design procedure to use Elman's RNN-based sequential controller is presented, and applied to different case studies. The proposed controller is tested experimentally and proves successful. Theoretical results as well as experimental results are presented and discussed indicating that the proposed design procedure using Elman's RNN can be effective in designing a sequential controller for different types of manufacturing systems.