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We propose a novel framework for designing a resilient Model PredictiveControl (MPC) targeting uncertain linear systems under cyber attack. Assuming aperiodic attack scenario, we model the system under Denial of Service (DoS)attack, also with measurement noise, as an uncertain linear system withparametric and additive uncertainty. To detect anomalies, we employ a Kalmanfilter-based approach. Then, through our observations of the intensity of thelaunched attack, we determine a range of possible values for the systemmatrices, as well as establish bounds of the additive uncertainty for theequivalent uncertain system. Leveraging a recent constraint tightening robustMPC method, we present an optimization-based resilient algorithm. Accordingly,we compute the uncertainty bounds and corresponding constraints offline forvarious attack magnitudes. Then, this data can be used efficiently in the MPCcomputations online. We demonstrate the effectiveness of the developedframework on the Adaptive Cruise Control (ACC) problem.

An Efficient Resilient MPC Scheme via Constraint Tightening against Cyberattacks: Application to Vehicle Cruise Control