Reinforcement Learning based temperature regulation for a Material Extrusion Additive Manufacturing system

With the rapid development of Additive Manufacturing (AM) comes an urgent need for advanced monitoring and control of the process. Many aspects of the AM process play a significant role in the efficiency, accuracy, and repeatability of the process, with temperature regulation being one of the most important. In this work, we solve the problem of optimal tracking control for a state space temperature model of a Big Area Additive Manufacturing (BAAM) system. In particular, we address the problem of designing a Linear Quadratic Tracking (LQT) controller when access to the exact system state is not possible, except in the form of measurements. We initially solve the problem with a model-based approach based on reinforcement learning concepts with state estimation through an observer. We then design a model-free reinforcement-learning based controller with an internal state estimation step and demonstrate its performance through a simulator of the systems’ behaviour. In addition, we explore the use of Bayesian Optimisation as a means to optimise the design parameters of the LQT problem. Our results showcase the possibility of achieving optimal behaviour while learning directly from process data, independently of a model of the process. This is an encouraging outcome towards the realisation of intelligent manufacturing.