This thesis provides an exposition of the theory of Active Inference in a control theoretic context. Active Inference is a remarkably powerful neuroscientific theory that unifies many characteristics of the biological brain. As such, Active Inference provides a valid inspiration in search of improvements in bio-inspired robot control algorithms. The literature on Active Inference however is narrow and complex. The goal of this thesis is to open the door research of Active Inference
in robotics, by applying the theory to linear state space systems and exposing the relations and differences with established engineering paradigms. We provide a detailed account of several critical details, mainly the concept of generalized motions, that are commonly not understood from the scientific literature. A start in the performance analysis of the algorithm is made, by studying the effect of changes in several tuning parameters. Additionally, with Active Inference reformulated as a state space control system, it is shown that standard behavior
such as stabilization and tracking can be achieved.