DeePC vs. Koopman MPC for Pasteurization: A Comparative Study

TL;DR

This study compares DeePC and Koopman MPC for controlling a pasteurization process, highlighting their differences in tracking accuracy and input smoothness using a neural-network digital twin.

Contribution

The paper provides a head-to-head comparison of DeePC and Koopman MPC on a multivariable process, revealing their respective strengths and trade-offs.

Findings

Koopman MPC tracks more tightly under the chosen cost.

DeePC produces substantially smoother input trajectories.

Both methods achieve feasible constrained control with comparable tracking error.

Abstract

Data-driven predictive control methods can provide the constraint handling and optimization of model predictive control (MPC) without first-principles models. Two such methods differ in how they replace the model: Data-enabled predictive control (DeePC) uses behavioral systems theory to predict directly from input--output trajectories via Hankel matrices, while Koopman-based MPC (KMPC) learns a lifted linear state-space representation from data. Both methods are well studied on their own, but head-to-head comparisons on multivariable process control problems are few. This paper compares them on a pasteurization unit with three manipulated inputs and three measured outputs, using a neural-network-based digital twin as the plant simulator. Both controllers share identical prediction horizons, cost weights, and constraints, so that differences in closed-loop behavior reflect the choice of predictive representation. Results show that both methods achieve feasible constrained control with comparable tracking error, but with a clear trade-off: KMPC tracks more tightly under the chosen cost, while DeePC produces substantially smoother input trajectories. These results help practitioners choose between the two approaches for thermal processing applications.