Dynamic Event-Triggered Model Predictive Control for Nonlinear Discrete Cyber-Physical Systems With Hybrid Attacks

This study presents a novel approach to dynamic event-triggered model predictive control (MPC) for nonlinear discrete-time cyber-physical systems (CPSs) subject to simultaneous hybrid denial of service (DoS) and deception attacks on the sensor-to-controller and controller-to-actuator communication channels. The proposed method introduces a dynamic event-triggered mechanism (DETM) with a predetermined offset to optimize communication efficiency in bandwidth-limited systems. Furthermore, a mechanism for detecting attacks has been developed to ensure the integrity of transmitted information, thereby mitigating the impact of hybrid attacks. Theoretical conditions are established to ensure the input-to-state practical stability of the closed-loop system using the proposed method. Finally, simulation cases are presented to validate the superiority of the proposed method in comparison to existing approaches in scenarios where CPSs encounter similar hybrid attacks. Note to Practitioners—This paper addresses the challenges of security control in nonlinear discrete information physical systems, such as robotic arms, mobile robots, and automotive systems. In CPSs, sensor and actuator components communicate over network channels, which can result in limited resources and vulnerabilities to tampering or delays due to network attacks, ultimately affecting system performance. Although MPC is effective, intermittent network attacks and inaccuracies in state estimation hinder the adjustment of control strategies. To tackle these issues, we propose a predictive control strategy that employs a dynamic event-triggering model with a detection mechanism. The method is validated through experiments conducted on a spring-damping system and a single-link flexible manipulator, demonstrating its potential for broader engineering applications. Future work will extend this strategy to multi-agent systems.