Next Article in Journal
Electromyography-Based Respiratory Onset Detection in COPD Patients on Non-Invasive Mechanical Ventilation
Next Article in Special Issue
Correction: Veen, D.; Stoel, D.; Schalken, N.; Mulder, K.; Van de Schoot, R. Using the Data Agreement Criterion to Rank Experts’ Beliefs. Entropy 2018, 20, 592
Previous Article in Journal
On Structural Entropy and Spatial Filling Factor Analysis of Colonoscopy Pictures
Previous Article in Special Issue
Hidden Node Detection between Observable Nodes Based on Bayesian Clustering
Open AccessArticle

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

EASY Group—Sussex Neuroscience, Department of Informatics, University of Sussex, Brighton BN1 9RH, UK
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in From Animals to Animats 15: 15th International Conference on Simulation of Adaptive Behavior, SAB 2018, Frankfurt/Main, Germany, 14–17 August 2018.
Entropy 2019, 21(3), 257; https://doi.org/10.3390/e21030257
Received: 18 January 2019 / Revised: 20 February 2019 / Accepted: 3 March 2019 / Published: 7 March 2019
(This article belongs to the Special Issue Bayesian Inference and Information Theory)
  |  
PDF [1763 KB, uploaded 27 March 2019]
  |  

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. View Full-Text
Keywords: approximate Bayesian inference; active inference; PID control; generalised state-space models; sensorimotor loops; information theory; control theory approximate Bayesian inference; active inference; PID control; generalised state-space models; sensorimotor loops; information theory; control theory
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top