# Model-agnostic stochastic model predictive control

arXiv: https://arxiv.org/abs/2211.13012 · HTML: https://mlsanity.com/paper/2211.13012/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2022.

## Built on by (most cited first, 5 of 5)

- [A Bayesian Framework for learning governing Partial Differential Equation from Data](https://mlsanity.com/paper/2306.04894/) (2023)
- [Discovering stochastic partial differential equations from limited data using variational Bayes inference](https://mlsanity.com/paper/2306.15873/) (2023)
- [MAntRA: A framework for model agnostic reliability analysis](https://mlsanity.com/paper/2212.06303/) (2022)
- [Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems](https://mlsanity.com/paper/2212.09240/) (2022)
- [Discovering interpretable Lagrangian of dynamical systems from data](https://mlsanity.com/paper/2302.04400/) (2023)

## Abstract (arXiv)

We propose a model-agnostic stochastic predictive control (MASMPC) algorithm for dynamical systems. The proposed approach first discovers textitinterpretable governing differential equations from data using a novel algorithm and blends it with a model predictive control algorithm. One salient feature of the proposed approach resides in the fact that it requires no input measurement (external excitation); the unknown excitation is instead treated as white noise, and a stochastic differential equation corresponding to the underlying system is identified. With the novel stochastic differential equation discovery framework, the proposed approach is able to generalize; this eliminates the repeated retraining phase -- a major bottleneck with other machine learning-based model agnostic control algorithms. Overall, the proposed MASMPC (a) is robust against measurement noise, (b) works with sparse measurements, (c) can tackle set-point changes, (d) works with multiple control variables, and (e) can incorporate dead time. We have obtained state-of-the-art results on several benchmark examples. Finally, we use the proposed approach for vibration mitigation of a 76-storey building under seismic loading.