# WSINDy for Model Predictive Control with Applications to Fusion, Drones, and Chaos

arXiv: https://arxiv.org/abs/2604.23269 · HTML: https://mlsanity.com/paper/2604.23269/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2026.

## Built on by (most cited first, 1 of 1)

- [Tensor-Train Weak SINDy: Identifying High-Dimensional Nonlinear Dynamics](https://mlsanity.com/paper/2609.09434/) (2026)

## Abstract (arXiv)

The control of complex dynamical systems remains a fundamental challenge in science and engineering, where strong nonlinearities, the presence of noise, and computational constraints often pose significant obstacles in traditional control approaches. Recent advances in data-driven methods, particularly system identification techniques, have shown a powerful alternative by providing fast, parsimonious, interpretable models that are well-suited for model predictive control (MPC). Building on these developments, the present article embeds WSINDy with actuation inputs (WSINDYc) within a MPC framework. Compared to benchmark data-driven methods, WSINDYc enables a more robust identification of the governing dynamics, particularly in the presence of high noise levels, resulting in more accurate and efficient control. The capabilities of the proposed WSINDY-MPC framework are demonstrated on a range of problems, including a tokamak plasma boundary model that includes main ion gas puff actuation, drone tracking and collision avoidance, the chaotic Lorenz system, and a simplified flight control model for an F-8 aircraft. The proposed framework achieves superior performance in the presence of noise, enabling longer prediction horizons, lower trajectory tracking error, and a more reliable obstacle clearance, while simultaneously achieving lower MPC cost values compared to the baseline methods.