Privacy for Switched Systems Under MPC: A Privacy-Preserved Rolling Optimization Strategy

Abstract

Differential privacy is an effective method to solve data privacy leakage. The common differential privacy method is achieved by adding privacy noises to the transmitted data, which may affect data accuracy. For the control system, data accuracy greatly affects the system performance. To circumvent this difficulty, we propose a novel privacy-preserved rolling optimization strategy (PP-ROS) for switched systems. The main contributions are reflected in three aspects: 1) The proposed PP-ROS is used to calculate the private control input by adding Laplace noise to the prediction and control horizons, instead of the transmitted data. 2) Privacy definitions of the prediction and control horizons are presented, and a private model predictive control (P-MPC) controller design is provided based on the PP-ROS. The P-MPC controller achieves the privacy of its parameters. 3) Under PP-ROS and P-MPC, the proof and calculation methods for the privacy levels of control input and system output are given. The results indicate that when noise is added to the horizons, both control input and system output are private. Finally, the availability and benefits of PP-ROS and P-MPC are demonstrated using two simulation examples and comparison results.