Privacy-Preserving Distributed Optimising Control of Water Supply
Authors
Term
4. semester
Education
Publication year
2024
Submitted on
2024-05-29
Pages
86
Abstract
Society relies on critical infrastructure, which, due to internet connections, is subject to an increasing threat of cyber attacks. This report presents a method for designing distributed optimising controllers for critical infrastructure, which keeps local cost functions and constraints private in case of passive and eavesdropping attacks, while protecting against specific active attacks. The method is based on distributing an Model Predictive Control problem using Alternating Direction Method of Multipliers, which is made privacy-preserving through the use of Secure Multi-Party Computation. A Water Distribution Network is emulated in a laboratory, where PI-controllers are implemented such that the Water Distribution Network can be modelled while abstracting from pump, valve, and pipe dynamics. Future actuation commands to the PI-controllers are found by optimising a non-convex Model Predictive Control problem with a cost function consisting of the electricity bill of the system, subject to system constraints. The optimisation problem is solved in a distributed manner, where the communication between the stakeholders is encrypted using Shamir’s Secret Sharing Scheme to obtain privacy-preserving features. The privacy-preserving controller finds minima with costs equal to an ordinary optimising controller. Thereby, the only cost of the privacy-preserving controller is longer computation time and increased communication.
Keywords
ADMM ; MPC ; SMPC ; Shamirs Secret sharing ; Secret sharing ; Consensus ; consensus ADMM ; Alternating Direction Method of Multipliers ; Secure Multiparty Computation ; Multiparty Computation ; Model Predictive Control ; Predictive control ; Optimising Control ; Water Supply ; SWIL ; Water Distribution Network ; Cyber Security ; Privacy preservation ; Distributed Control
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