2022/04/04 by Ajith Anil Meera, Meera, Ajith Anil, Martijn Wisse +1
Computer Science · Neuroscience · #FOS: Computer and information sciences #FOS: Electrical engineering #Neural Networks and Applications #Neural dynamics and brain function #Neuroscience and Music Perception #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2204.01796
openalex publication_date 2022/04/04 · openalex created_date 2022/04/27 · openalex updated_date 2026/07/28
The free energy principle (FEP) from neuroscience provides a framework called active inference for the joint estimation and control of state space systems, subjected to colored noise. However, the active inference community has been challenged with the critical task of manually tuning the noise smoothness parameter. To solve this problem, we introduce a novel online noise smoothness estimator based on the idea of free energy principle. We mathematically show that our estimator can converge to the free energy optimum during smoothness estimation. Using this formulation, we introduce a joint state and noise smoothness observer design called DEMs. Through rigorous simulations, we show that DEMs outperforms state-of-the-art state observers with least state estimation error. Finally, we provide a proof of concept for DEMs by applying it on a real life robotics problem - state estimation of a quadrotor hovering in wind, demonstrating its practical use.