# On Information Transfer in Control Dynamical Systems

arXiv: https://arxiv.org/abs/1704.05370 · HTML: https://mlsanity.com/paper/1704.05370/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2017.

## Built on by (most cited first, 3 of 3)

- [Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic](https://mlsanity.com/paper/2111.12272/) (2021)
- [Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms](https://mlsanity.com/paper/2112.03407/) (2021)
- [Data-driven Influence Based Clustering of Dynamical Systems](https://mlsanity.com/paper/2204.02373/) (2022)

## Abstract (arXiv)

In this paper, we show through examples, how the existing definitions of information transfer, namely directed information and transfer entropy fail to capture true causal interaction between states in control dynamical system. We propose a new definition of information transfer, based on the ideas from dynamical system theory, and show that this new definition can capture true causal interaction between states. The information transfer measure is generalized to define transfers between the various signals in a control dynamical system and analytical expression for information transfer between state-to-state, input-to-state, state-to-output, and input-to-output are provided for linear systems. There is a natural extension of our proposed definition to define information transfer over n time steps and average information transfer over infinite time step. We show that the average information transfer in feedback control system between plant output and input is equal to the entropy of the open loop dynamics thereby re-deriving the Bode fundamental limitation results using the proposed definition of transfer.