Security-Based Distributionally Robust Optimization Self-Triggered SMPC for Constrained Cyber–Physical Systems Subject to Unknown Disturbances and Denial-of-Service Attacks

In this article, we propose a distributionally robust optimization-based self-triggered stochastic model predictive control (DRSMPC) algorithm for linear discrete systems that are subject to unbounded stochastic disturbances and Denial-of-Service (DoS) attacks. Assuming that only the first and second moments of the disturbances are available, we transform the objective function into a compact quadratic form and reformulate the state chance constraint into second-order cone constraint, which enhance tractability during the solution process. To reduce communication and sampling times within the system, we introduce a self-triggering update framework, which computes the sampling instants and the control input sequences between consecutive sampling instants based on the sampled state. The main contribution of DRSMPC is to deal with unknown disturbances, reduce the unnecessary sampling also be able to defend against DoS attacks. Furthermore, we demonstrate that DRSMPC is recursively feasible and cyber-physical systems (CPSs) remaining quadratic stability. Numerical simulations validate the effectiveness of the proposed algorithm.