Self-Triggered Switched ISS Framework Under Computational Weaponization

Networked Cyber–Physical Systems (CPSs), like autonomous quadrotor swarms, tightly couple continuous physical kinematics, wireless information exchange, and discrete real-time task scheduling. While conventional consensus security architectures focus exclusively on data-layer falsification, they fundamentally decouple adversarial behavior from onboard computational resource state profiles. This paper addresses a core CPS vulnerability termed Computational Weaponization, the deliberate injection of complex computational workloads (adversarial LLM token parsing or cryptographic verification) to intentionally manipulate hardware execution delays. Through this exploit, strategic cyber–physical perturbations force resource-constrained embedded microcontrollers to saturate their task execution queues, inducing real-time scheduling starvation and physical tracking divergence. To mitigate this without optimization bottlenecks, we present a state-dependent, Self-Triggered Control (STC) and Prospect Theoretic Alignment (PTA) co-design framework. The proposed protocol models the hardware microprocessor’s execution delay as an endogenous dynamic state coupled directly to continuous tracking spaces. By mapping discrete topology reconfigurations and variable task delays to a switched impulsive time-delay system, we leverage an Input-to-State Stability (ISS) to derive sufficient linear matrix inequality conditions. We prove that the coupled cyber–physical–computational loop achieves asymptotic consensus and bounded trajectory containment under adversarial actions.