  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
ones. 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.
Our results showcase the possibility of achieving comparable results while
learning optimal policies directly from process data, without the need for an
accurate, intricate model of the process. We consider this outcome to be a
significant stride towards autonomous intelligent manufacturing.
