Protocol-Based Model Predictive Control for Networked Switching Systems With Piecewise-Homogeneous Sojourn Probabilities

Networked switching systems, which integrate multiple subsystems controlled by switching signals, play a crucial role in modern cyber-physical applications such as industrial automation and smart grids. However, their performance is often limited by constrained communication bandwidth and complex dynamic interactions. To address these challenges, this paper proposes a protocol-based model predictive control (MPC) framework for networked switching systems with piecewise-homogeneous sojourn probabilities. A dynamically matching mechanism is designed to quantify mode mismatches caused by network-induced uncertainties. Additionally, an adaptive dynamic-memory event-triggered protocol (ADMETP) is developed, which leverages historical data to optimize triggering decisions and dynamically adjusts thresholds to reduce communication overhead while maintaining system stability. Sufficient conditions for mean-square exponential stability are derived using Lyapunov theory, providing rigorous theoretical guarantees. The effectiveness of the approach is validated through simulations of a numerical experiment and an RLC circuit, demonstrating superior resource utilization and control performance compared to existing methods. This work bridges the gap between adaptive resource management and robust control in networked switching systems, offering practical insights for applications with constrained communication resources.