Learning-based adaptive controller for dynamic manufacturing cells

Abstract

Due to the complexity, uncertainty and dynamics in the modern manufacturing environment, a flexible and adaptive cell controller is essential to achieve the system production goals. The paper proposes a learning-based approach for the adaptive controller, which will receive feedback on current performance from the cell, and fine-tune its knowledge base by using a cerebellar model articulation controller (CMAC) network. To examine the proposed controller's performance in manufacturing cells, several experiments are conducted based on simulation. The results show that the controller performs well under multiple (conflicting) performance measures. Furthermore, it is shown that the controller with feedback can learn and adapt to changing environments. Most interestingly, the paper also demonstrates that the proposed controller can adapt to changing system objectives (desired performance measures).