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Cloud-Based Privacy-Preserving Robust Model Predictive Control Using Semi-Homomorphic Encryption
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DOI：10.1002/rnc.70339.png)
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Abstract
En 中文
In cloud-based control architectures, a robust model predictive control (RMPC) using semi-homomorphic encryption is proposed to ensure the stability of polytopic uncertain systems while preserving system privacy and security. Firstly, a cloud-based encrypted RMPC (ERMPC) framework is proposed, and semi-homomorphic encryption is utilized to enable encrypted evaluation of control inputs. Secondly, an optimization problem is formulated to address both model uncertainties and quantization-induced system errors, ensuring closed-loop stability and invariance through the notion of quadratic boundedness. Thirdly, an ERMPC algorithm is presented, and conditions for selecting encryption-related parameters are provided to guarantee the stability and bounded control performance of the encrypted cloud-based control systems. Finally, a numerical example is conducted to demonstrate the effectiveness of the proposed approach in enhancing privacy and security in cloud-based control systems.
Keywords:
cyber-security
encrypted control
homomorphic encryption
privacy
robust model predictive control
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