Robust Model Predictive Control of Cyber-Physical Linear Parameter Varying System subject to deception attacks and bounded disturbances

The purpose of this study is to develop a robust model predictive control (RMPC) strategy for cyber-physical systems (CPSs) that are subject to bound disturbances and deception attacks. Based on the assumption that deception attacks have norm-bounded structures, a model of attack has been developed using Bernoulli-distributed white sequences. Furthermore, we propose a novel observer-based augmented closed-loop controller that performs two main functions. The first step in estimating the state of each component of the system is to estimate their current condition. As a second step, it is necessary to ensure that some unprotected communication channels are prevented from having an adverse deception attack effect on the system's response. Using estimated states, an output-feedback controller is investigated for the detection of cyber-attacks. Under the proposed approach, all CPS states under deception attacks appear to be convergent. Using linear matrix inequalities, the requirement is formulated. To demonstrate the applicability of the proposed methodology, simulation experiments have been performed on cloud-aided active suspension systems.