Input‐To‐State Stabilizing Extreme Learning Machine‐Based Model Predictive Control Under Denial‐Of‐Service Attack

We present a novel model predictive control (MPC) strategy integrating extreme learning machine (ELM) modeling to protect discrete‐time systems against denial‐of‐service (DoS) attacks. Our approach employs ELM to construct data‐driven prediction models, significantly reducing modeling costs compared to traditional methods. To ensure closed‐loop stability under DoS attacks, we develop a specialized terminal positive invariant set that handles the linearization remainder terms from ELM‐represented nonlinear systems. We establish theoretical guarantees of input‐to‐state stability (ISS) for the closed‐loop system under bounded prediction errors. Simulations on a continuous stirred tank reactor (CSTR) and implementation on a three‐wheeled omnidirectional robot platform demonstrate the effectiveness of our approach while maintaining stability under sustained DoS attacks.