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.
|Title of host publication||Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022)|
|Publication status||Published - 2022|
|Event||IEEE 61st Conference on Decision and Control (CDC 2022) - Cancún, Mexico|
Duration: 6 Dec 2022 → 9 Dec 2022
|Conference||IEEE 61st Conference on Decision and Control (CDC 2022)|
|Period||6/12/22 → 9/12/22|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Linear systems
- Stability analysis
- Mathematical models
- Colored noise
- Task analysis