Variational Encrypted Model Predictive Control
This addresses secure control for systems requiring privacy, but it appears incremental as it builds on existing encrypted MPC methods.
The paper tackled the problem of secure online control by developing a variational encrypted model predictive control protocol that reduces encrypted computation through a sampling-based estimator, with simulation results showing practical applicability.
We develop a variational encrypted model predictive control (VEMPC) protocol whose online execution relies only on encrypted polynomial operations. The proposed approach reformulates the MPC problem into a sampling-based estimator, in which the computation of the quadratic cost is naturally handled by tilting the sampling distribution, thus reducing online encrypted computation. The resulting protocol requires no additional communication rounds or intermediate decryption, and scales efficiently through two complementary levels of parallelism. We analyze the effect of encryption-induced errors on optimality, and simulation results demonstrate the practical applicability of the proposed method.