Preprint Article Version 1 This version is not peer-reviewed

PID Control as a Process of Active Inference with Linear Generative Model

Version 1 : Received: 25 February 2019 / Approved: 27 February 2019 / Online: 27 February 2019 (05:00:10 CET)

A peer-reviewed article of this Preprint also exists.

Baltieri, M.; Buckley, C.L. PID Control as a Process of Active Inference with Linear Generative Models. Entropy 2019, 21, 257. Baltieri, M.; Buckley, C.L. PID Control as a Process of Active Inference with Linear Generative Models. Entropy 2019, 21, 257.

Journal reference: Entropy 2019, 21, 257
DOI: 10.3390/e21030257

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 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 provides also 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.

Subject Areas

approximate Bayesian inference; active inference; PID control; generalised state-space models; sensorimotor loops; information theory; control theory

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.