# Information-seeking polynomial NARX model-predictive control through expected free energy minimization

arXiv: https://arxiv.org/abs/2312.15046 · HTML: https://mlsanity.com/paper/2312.15046/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [Active Inference for Integrated State-Estimation, Control, and Learning](https://mlsanity.com/paper/2005.05894/) (2020)
- [Active Inference and Epistemic Value in Graphical Models](https://mlsanity.com/paper/2109.00541/) (2021)
- [Application of the Free Energy Principle to Estimation and Control](https://mlsanity.com/paper/1910.09823/) (2019)
- [On the Simulation of Polynomial NARMAX Models](https://mlsanity.com/paper/1810.06883/) (2018)
- [Learning Policies for Continuous Control via Transition Models](https://mlsanity.com/paper/2209.08033/) (2022)
- [Variational message passing for online polynomial NARMAX identification](https://mlsanity.com/paper/2204.00769/) (2022)

## Built on by (most cited first, 4 of 4)

- [Confidence-Aware Decision-Making and Control for Tool Selection](https://mlsanity.com/paper/2403.03808/) (2024)
- [Coupled autoregressive active inference agents for control of multi-joint dynamical systems](https://mlsanity.com/paper/2410.10415/) (2024)
- [Bayesian autoregression to optimize temporal Mat'ern kernel Gaussian process hyperparameters](https://mlsanity.com/paper/2508.09792/) (2025)
- [Message passing-based inference in an autoregressive active inference agent](https://mlsanity.com/paper/2509.25482/) (2025)

## Abstract (arXiv)

We propose an adaptive model-predictive controller that balances driving the system to a goal state and seeking system observations that are informative with respect to the parameters of a nonlinear autoregressive exogenous model. The controller's objective function is derived from an expected free energy functional and contains information-theoretic terms expressing uncertainty over model parameters and output predictions. Experiments illustrate how parameter uncertainty affects the control objective and evaluate the proposed controller for a pendulum swing-up task.