# Image-Based Model Predictive Control via Dynamic Mode Decomposition

arXiv: https://arxiv.org/abs/2006.06727 · HTML: https://mlsanity.com/paper/2006.06727/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

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

- [Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing](https://mlsanity.com/paper/2210.07848/) (2022)
- [SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control](https://mlsanity.com/paper/2202.01816/) (2022)
- [Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison](https://mlsanity.com/paper/2506.12819/) (2025)

## Abstract (arXiv)

We present a data-driven model predictive control (MPC) framework for systems with high state-space dimensionalities. This work is motivated by the need to exploit sensor data that appears in the form of images (e.g., 2D or 3D spatial fields reported by thermal cameras). We propose to use dynamic mode decomposition (DMD) to directly build a low-dimensional model from image data and we use such model to obtain a tractable MPC controller. We demonstrate the scalability of this approach (which we call DMD-MPC) by using a 2D thermal diffusion system. Here, we assume that the evolution of the thermal field is captured by 50x50 pixel images, which results in a 2500-dimensional state-space. We show that the dynamics of this high-dimensional space can be accurately predicted by using a 40-dimensional DMD model and we show that the field can be manipulated satisfactorily by using an MPC controller that embeds the low-dimensional DMD model. We also show that the DMD-MPC controller significantly outperforms a standard MPC controller that uses data from a finite set of spatial locations (proxy locations) to manipulate the high-dimensional thermal field. This comparison illustrates the value of information embedded in image data.