Computationally Efficient Predictive Control Using SINDy Models

doi:10.3390/electronics15122530

Computationally Efficient Predictive Control Using SINDy Models

2026-06-08

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DOI：10.3390/electronics15122530

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The Sparse Identification of Nonlinear Dynamics (SINDy) method yields compact and interpretable models that preserve physical system properties, offering a superior alternative to black-box models. This work proposes a computationally efficient Model Predictive Control (MPC) algorithm for SINDy models. The algorithm employs a successively obtained online linear Taylor approximation of the model for future prediction, while the full SINDy model captures past dynamics. As a result, the nonlinear MPC problem is reformulated as a tractable quadratic program. The implementation covers three discretization schemes: the first-order Euler and the simplified and full fourth-order Runge–Kutta. Simulation benchmarks for population dynamics and aircraft models show that the algorithm achieves performance comparable to nonlinear MPC with significantly lower complexity, enabling real-time use.

Keywords:

Sparse Identification of Nonlinear Dynamics (SINDy)

Model Predictive Control (MPC)

data-driven modeling

Computationally Efficient Predictive Control Using SINDy Models

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