Model Predictive Control as a Secure Service for Cyber–Physical Systems: A Cloud-Edge Framework

Abstract

This article proposes a model predictive control as a secure service (MPCaaSS) framework for cyber–physical systems (CPSs) in the presence of both cyber threats and external disturbances. First, in order to take advantage of the cloud-edge computing, we design a double-layer controller architecture by using a novel control parameterization based on Gaussian radial basis functions. In this controller architecture, the cloud-side controller optimizes the controller parameters of the edge-side controller, whereas the edge-side controller implements the real-time control law using the generated controller parameters. Second, in order to securely transmit data packets, we integrate an encoding scheme and an elliptic curve cryptography (ECC)-based encryption into the proposed MPCaaSS framework. Then, the controller parameters and the state measurements can be encrypted such that no malicious attackers can corrupt and intercept the transmission. It is shown that the recursive feasibility of MPCaaSS is achieved under some sufficient conditions, and the robust stability of the closed-loop system is guaranteed if the optimization problem is recursively feasible. Simulated examples are conducted to demonstrate the effectiveness of the proposed method.