scholarly journals A Probabilistic Interpretation of PID Controllers Using Active Inference

2018 ◽  
pp. 15-26 ◽  
Author(s):  
Manuel Baltieri ◽  
Christopher L. Buckley
Keyword(s):  
Pid Controllers ◽  
Probabilistic Interpretation ◽  
Active Inference

scholarly journals A probabilistic interpretation of PID controllers using active inference

2018 ◽  
Author(s):  
Manuel Baltieri ◽  
Christopher L. Buckley
Keyword(s):  
Free Energy ◽  
Control Theory ◽  
Biological Systems ◽  
Generative Models ◽  
Pid Controllers ◽  
Probabilistic Interpretation ◽  
Active Inference ◽  
General Process ◽  
Brain Functions ◽  
And Control

AbstractIn the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. The Bayesian brain hypothesis, predictive coding, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to unify understandings of life and cognition within general mathematical frameworks derived from information theory, statistical physics and machine learning. Furthermore, it has been argued that one such proposal, active inference, combines both information and control theory and has its roots in cybernetics studies of the brain. The connections between information and control theory have been discussed since the 1950’s by scientists like Shannon and Kalman and have recently risen to prominence in modern stochastic optimal control theory. How-ever, the implications of the confluence of these two theoretical frame-works for the biological sciences have been slow to emerge. Here we argue that if the active inference proposal is to be taken as a general process theory for biological systems, we need to consider how existing control theoretical approaches to biological systems relate to it. In this work we will focus on PID (Proportional-Integral-Derivative) controllers, one of the most common types of regulators employed in engineering and more recently used to explain behaviour in biological systems, e.g. chemotaxis in bacteria and amoebae or robust adaptation in biochemical networks. Using active inference, we derive a probabilistic interpretation of PID controllers, showing how they can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation once we use only simple linear generative models.

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

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 257 ◽  
Author(s):  
Manuel Baltieri ◽  
Christopher Buckley
Keyword(s):  
Free Energy ◽  
Biological Systems ◽  
Generative Models ◽  
Pid Controllers ◽  
Mathematical Framework ◽  
Prediction Errors ◽  
Active Inference ◽  
General Process ◽  
Brain Functions ◽  
Theoretical Approaches

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.

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

2019 ◽  
Author(s):  
Manuel Baltieri ◽  
Christopher L. Buckley
Keyword(s):  
Free Energy ◽  
Pid Control ◽  
Biological Systems ◽  
Generative Models ◽  
Pid Controllers ◽  
Mathematical Framework ◽  
Active Inference ◽  
General Process ◽  
Brain Functions ◽  
Theoretical Approaches

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.

Tinkering with cognitive gadgets: Cultural evolutionary psychology meets active inference

2019 ◽  
Vol 42 ◽  
Author(s):  
Paul Benjamin Badcock ◽  
Axel Constant ◽  
Maxwell James Désormeau Ramstead
Keyword(s):  
Evolutionary Psychology ◽  
Research Program ◽  
Active Inference ◽  
Innovative Research ◽  
Cognitive Faculties ◽  
Cultural Evolutionary ◽  
The Relationship

Abstract Cognitive Gadgets offers a new, convincing perspective on the origins of our distinctive cognitive faculties, coupled with a clear, innovative research program. Although we broadly endorse Heyes’ ideas, we raise some concerns about her characterisation of evolutionary psychology and the relationship between biology and culture, before discussing the potential fruits of examining cognitive gadgets through the lens of active inference.

Development of a Jacobian Module for Advanced Pulp and Paper Simulation and Control

TAPPI Journal ◽  
2009 ◽  
Vol 8 (1) ◽  
pp. 4-11
Author(s):  
MOHAMED CHBEL ◽  
LUC LAPERRIÈRE
Keyword(s):  
Control System ◽  
Nonlinear Behavior ◽  
Simulation Software ◽  
Pulp And Paper ◽  
Pid Controllers ◽  
Tuning Parameters ◽  
Pid Tuning ◽  
Fixed Set ◽  
And Control ◽  
Operating Points

Pulp and paper processes frequently present nonlinear behavior, which means that process dynam-ics change with the operating points. These nonlinearities can challenge process control. PID controllers are the most popular controllers because they are simple and robust. However, a fixed set of PID tuning parameters is gen-erally not sufficient to optimize control of the process. Problems related to nonlinearities such as sluggish or oscilla-tory response can arise in different operating regions. Gain scheduling is a potential solution. In processes with mul-tiple control objectives, the control strategy must further evaluate loop interactions to decide on the pairing of manipulated and controlled variables that minimize the effect of such interactions and hence, optimize controller’s performance and stability. Using the CADSIM Plus™ commercial simulation software, we developed a Jacobian sim-ulation module that enables automatic bumps on the manipulated variables to calculate process gains at different operating points. These gains can be used in controller tuning. The module also enables the control system designer to evaluate loop interactions in a multivariable control system by calculating the Relative Gain Array (RGA) matrix, of which the Jacobian is an essential part.

An Adjustment of Reference Tracking PID Controllers for a First-order System with a Dead Time

2016 ◽  
Vol 136 (5) ◽  
pp. 676-682 ◽  
Author(s):  
Akihiro Ishimura ◽  
Masayoshi Nakamoto ◽  
Takuya Kinoshita ◽  
Toru Yamamoto
Keyword(s):  
Dead Time ◽  
Order System ◽  
Pid Controllers ◽  
Reference Tracking ◽  
First Order

New Designing Approaches for Quadcopter PID Controllers Using Reference Model and Genetic Algorithm Techniques

2017 ◽  
Vol 10 (3) ◽  
pp. 240 ◽  
Author(s):  
Imane Siti ◽  
Mostafa Mjahed ◽  
Hassan Ayad ◽  
Abdeljalil El Kari
Keyword(s):  
Genetic Algorithm ◽  
Reference Model ◽  
Pid Controllers

Narrative as active inference

2020 ◽  
Author(s):  
Nabil Bouizegarene ◽  
maxwell ramstead ◽  
Axel Constant ◽  
Karl Friston ◽  
Laurence Kirmayer
Keyword(s):  
Well Being ◽  
Social Practices ◽  
Human Adaptation ◽  
Active Inference ◽  
Future Projection ◽  
Future Developments ◽  
The Social ◽  
Parsimonious Model ◽  
Living Organisms ◽  
Theory Research

The ubiquity and importance of narratives in human adaptation has been recognized by many scholars. Research has identified several functions of narratives that are conducive to individuals’ well-being and adaptation as well as to coordinated social practices and enculturation. In this paper, we characterize the social and cognitive functions of narratives in terms of the framework of active inference. Active inference depicts the fundamental tendency of living organisms to adapt by creating, updating, and maintaining inferences about their environment. We review the literature on the functions of narratives in identity, event segmentation, episodic memory, future projection, storytelling practices, and enculturation. We then re-cast these functions of narratives in terms of active inference, outlining a parsimonious model that can guide future developments in narrative theory, research, and clinical applications.

Sign in / Sign up

Export Citation Format

Share Document