Encrypted machine learning-based model predictive control architectures for nonlinear systems

Arthur Khodaverdian

doi:10.1016/j.compchemeng.2025.109166

Encrypted machine learning-based model predictive control architectures for nonlinear systems

2025-06-06

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Panagiotis D. Christofides *

DOI：10.1016/j.compchemeng.2025.109166

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Abstract

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This work proposes the implementation of encryption in model predictive control of nonlinear systems in which the system dynamics are modeled through machine-learning, denoted ML-based MPC, as a means to improve cybersecurity without significant performance losses. The Pallier cryptosystem is utilized for encryption and the closed-loop stability of the encrypted ML-based MPC is established accounting for the impacts of signal quantization loss due to encryption and sample-and-hold control. A nonlinear chemical process example is used to study the impact of different encryption levels on ML-based MPC closed-loop performance. Finally, we present the implementation of the encrypted ML-based MPC method in a two-layer economic model predictive control framework and in a distributed model predictive control scheme to optimize economic performance and control large-scale processes, respectively.

Keywords:

Encrypted control

Model predictive control

Machine learning

Nonlinear systems

Semi-homomorphic encryption

Encrypted machine learning-based model predictive control architectures for nonlinear systems

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