A Novel Adaptive Controller for Robot Manipulators Based on Active Inference
More adaptive controllers for robot manipulators are needed, which can deal with large model uncertainties. This letter presents a novel active inference controller (AIC) as an adaptive control scheme for industrial robots. This scheme is easily scalable to high degrees of freedom, and it maintains high performance even in the presence of large unmodeled dynamics. The proposed method is based on active inference, a promising neuroscientific theory of the brain, which describes a biologically plausible algorithm for perception and action. In this work, we formulate active inference from a control perspective, deriving a model free control law which is less sensitive to unmodeled dynamics. The performance and the adaptive properties of the algorithm are compared to a state of the art model reference adaptive controller (MRAC) in an experimental setup with a real 7 DOF robot arm. The results showed that the AIC outperformed the MRAC in terms of adaptability, providing a more general control law. This confirmed the relevance of active inference for robot control.
IEEE Robotics and Automation Letters