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

Manuel Baltieri1, Christopher L Buckley1

  • 1EASY Group-Sussex Neuroscience, Department of Informatics, University of Sussex, Brighton BN1 9RH, UK.

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Summary
This summary is machine-generated.

This study connects Proportional-Integral-Derivative (PID) control to the free energy principle, a unified theory for life and cognition. It shows how PID controllers can minimize free energy, offering new insights into biological systems and control theory challenges.

Keywords:
PID controlactive inferenceapproximate Bayesian inferencecontrol theorygeneralised state-space modelsinformation theorysensorimotor loops

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Control Theory
  • Statistical Mechanics

Background:

  • Probabilistic interpretations of brain function are prevalent in cognitive science and neuroscience.
  • The free energy principle and active inference offer a unified mathematical framework for cognition and life.
  • Existing control theoretical approaches are widely used to explain biological systems.

Purpose of the Study:

  • To investigate the relationship between active inference and existing control theoretical approaches in biological systems.
  • To demonstrate how Proportional-Integral-Derivative (PID) controllers align with the free energy principle.
  • To offer new perspectives on PID controller challenges within a broader theoretical framework.

Main Methods:

  • Formulating PID controllers within the framework of variational free energy minimization.
  • Utilizing approximate linear generative models of the world.
  • Analyzing PID controller parameter tuning and performance-robustness trade-offs.

Main Results:

  • PID controllers can be interpreted as minimizing free energy under specific modeling assumptions.
  • This interpretation provides a unified view of PID control within a general theory of cognition.
  • Traditional PID control problems are reframed in terms of optimizing precision in prediction errors.

Conclusions:

  • PID control mechanisms can be integrated into the broader theoretical framework of the free energy principle.
  • This integration offers novel insights into the functioning and optimization of biological control systems.
  • The study bridges the gap between engineering control theory and theoretical neuroscience/cognitive science.