Dealing with inherently unmodeled dynamics and large parameter variations or faults, is a challenging task while controlling robot manipulators. Classical control techniques cannot usually provide satisfactory responses, and often external supervision systems have to be designed to handle the faults. Recent research has shown that active inference, a unifying neuroscientific theory of the brain, bares the potential of intrinsically coping with strong uncertainties in the system, mimicking the adaptability capabilities of humans. However, the current state-of-the-art regarding active inference in robotics is very narrow and limited. This thesis presents a novel active inference controller as a general adaptive fault tolerant solution for control of robot manipulators. The goal of this work is threefold. First, we demonstrate the applicability of active inference in robotics, deriving a control scheme which is computationally efficient and with high performance. Second, we verify the claimed adaptability properties of active inference against a model reference adaptive controller, in a simulated on-line pick and place task with a 7 degrees-of-freedom robot arm. Third, we propose a method to exploit the controller's structure to perform fault detection, isolation and recovery, without the use of external supervision systems. This work showed that not only active inference is applicable to robotics, but it also outperforms the model reference adaptive controller, and it allows to efficiently deal with sensory faults. This thesis represents a leap forward with respect to the current state-of-the-art of active inference for robotics, and it lays the foundations for further research in this direction.