A LEARNING-BASED REAL-TIME CONTROLLER FOR DYNAMIC MANUFACTURING CELLS

ABSTRACT

A flexible controller (scheduling system) is essential if multiple performance objectives are to be met and uncertainty handled during production. In this paper, a learning-based, real-time scheduling system for controlling a manufacturing cell is presented. To develop the controller, an off-line module utilizes simulation to generate training samples to initialize the knowledge bases within the control system. During the operation, the difference between the performance level and the output from knowledge bases will be used to update and maintain the knowledge bases through reinforcement learning. To accomplish both off-line training and reinforcement learning, a CMAC (Cerebellar Model Articulation Controller) network is adopted to develop each knowledge base. The experimental results from simulation show that the controller performs well under multiple criterion environments. The simulation results showed improvement in system performance when reinforcement learning is incorporated in the feedback loop.

Keywords:

• Dynamic Scheduling
• Learning
• Adaptability