AI 中文总结
研究加密模型预测控制设计问题，提出利用最优控制律多项式近似的新方法，在全同态加密框架内评估显式控制律，经实验室规模实验验证，该方法能保护数据和系数，具有优势。
AI 中文摘要
本文重新审视加密模型预测控制（MPC）设计问题，这在近期安全过程控制领域是重大挑战。现有基于安全优化的控制方法不存在，部分实现也无法解决约束MPC的闭环系统稳定性和递归可行性。为克服这些局限，我们提出利用最优控制律多项式近似的新方法。该方法在全同态加密框架内评估显式控制律，保护过程数据和控制器系数。实验室规模实施和验证的实验结果证明了该隐私感知控制方法的优势。
英文摘要
In this paper, we revisit the problem of an encrypted model predictive control (MPC) design, representing a significant challenge in the recent field of secure process control. Existing methods in secure optimization-based control are non-existent and even partial implementation fails to address the closed-loop system stability and recursive feasibility properties of the constrained MPC. To overcome these limitations, we propose a novel approach that utilizes a polynomial approximation of the optimal control law. This method evaluates the explicit control law within a fully homomorphic encryption framework, ensuring that the controller is securely deployed on any third-party or cloud-based platform, with both process data and controller coefficients protected. Experimental results from a laboratory-scale implementation and validation of the proposed privacy-aware control method demonstrate its advantages.
