Encrypted Machine Learning-Based Model Predictive Control of Nonlinear Systems

Arthur Khodaverdian, Guoquan Wu, Zhe Wu, Panagiotis D. Christofides

Abstract: This work proposes the implementation of encryption in machine learning-based model predictive control (ML-based MPC) of nonlinear systems 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 with an unstable operating steady-state is used to study the impact of different encryption levels on ML-based MPC closed-loop performance.

External IDs:dblp:conf/amcc/KhodaverdianWWC25