PID Control as a Process of Active Inference with Linear Generative Models.
PID Control as a Process of Active Inference with Linear Generative Models.
复制标题
DOI：
10.3390/e21030257
复制
发表时间：
2019-03-07
期刊：
影响因子：
--
通讯作者：
Buckley CL
中科院分区：
文献类型：
--
作者：
Baltieri M;Buckley CL
在过去的几十年里，对大脑功能的概率解释在认知科学和神经科学中得到了广泛的应用。特别是，自由能原理和主动推理是日益流行的认知功能理论，声称在从信息和控制理论以及统计力学衍生的一般数学框架内提供对生命和认知的统一理解。然而，我们认为，如果要将主动推理建议作为生物系统的一般过程理论，就有必要了解它与常规用于研究和解释生物系统的现有控制理论方法之间的关系。例如，最近，比例-积分-导数(比例-积分-导数)控制已被证明在简单的分子系统中实施，并正在成为一种流行的机制来解释行为，如细菌和阿米巴的趋化性，以及生物化学网络中的稳健适应。在这项工作中，我们将展示当使用世界的近似线性生成模型时，在(变分)自由能最小化原则下，PID控制器如何适应更一般的生命和认知理论。这种更一般的解释也为传统的PID控制器问题提供了一个新的视角，例如参数整定以及平衡控制器的性能和鲁棒性条件的需要。具体地说，我们然后展示了如何通过优化精度(逆方差)来理解这些问题，这些精度(逆方差)调制了自由能泛函中的不同预测误差。
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.
登录
查看更多内容
影响因子：
1.8
作者：
Bogacz R
通讯作者：
Bogacz R
影响因子：
6.4
作者：
Astrom, KJ;Panagopoulos, H;Hagglund, T
通讯作者：
Hagglund, T
影响因子：
2.9
作者：
DAYAN, P;HINTON, GE;ZEMEL, RS
通讯作者：
ZEMEL, RS
影响因子：
4.9
作者：
Åström, KJ;Hägglund, T
通讯作者：
Hägglund, T
影响因子：
2
作者：
Ang, Jordan;Bagh, Sangram;McMillen, David R.
通讯作者：
McMillen, David R.