An Adaptive Event-Triggered Predictive Control Method

This paper studies an adaptive event-triggered (AET) control method for sampled-data systems to enhance communication efficiency and conserve resources. Initially, a dynamic event-triggering mechanism is introduced to minimize redundant data transmissions while maintaining system performance. Then, a state feedback control law is designed by optimizing the event-triggered threshold condition, with the system's stability verified using a Lyapunov function. The gain parameters are computed through a cone complementarity linearization algorithm, ensuring computational efficiency and robustness. Additionally, the proposed approach addresses the trade-off between communication resource utilization and control performance by dynamically adjusting the triggering conditions based on system states. This ensures a reduction in redundant transmissions without compromising system reliability. Finally, this method provides a systematic and effective framework for improving communication efficiency, with potential applications in industrial automation and resource-constrained networked control systems.