ArticleEntropy (Basel, Switzerland)2019
PID Control as a Process of Active Inference with Linear Generative Models.
Article in Entropy (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 66 citations in OpenAlex.
- Active Inference and Functional Parametrisation: Differential Flatness and Smooth Random Realisation.Entropy (Basel, Switzerland) · 2026Article
- When prediction fails: a computational complexity view of stress.Frontiers in veterinary science · 2026Article
- Five Fristonian Formulae.Entropy (Basel, Switzerland) · 2025Article
- Evaluation of flow control using PID versus fuzzy logic in an electropneumatic circuit for pulmonary ventilation applications.PloS one · 2025Article
- Integration of Sense and Control for Uncertain Systems Based on Delayed Feedback Active Inference.Entropy (Basel, Switzerland) · 2024Article
- Article
- Collective behavior from surprise minimization.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- How the conception of control influences our understanding of actions.Nature reviews. Neuroscience · 2023Review
- Turn-Taking Mechanisms in Imitative Interaction: Robotic Social Interaction Based on the Free Energy Principle.Entropy (Basel, Switzerland) · 2023Article
- Flexible intentions: An Active Inference theory.Frontiers in computational neuroscience · 2023Article
- How Active Inference Could Help Revolutionise Robotics.Entropy (Basel, Switzerland) · 2022Article
- Bayesian mechanics for stationary processes.Proceedings. Mathematical, physical, and engineering sciences · 2021Article
- On Epistemics in Expected Free Energy for Linear Gaussian State Space Models.Entropy (Basel, Switzerland) · 2021Article
- Dynamic Expectation Maximization Algorithm for Estimation of Linear Systems with Colored Noise.Entropy (Basel, Switzerland) · 2021Article
- The computational neurology of movement under active inference.Brain : a journal of neurology · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID (Proportional-Integral-Derivative) control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation also provides a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.