Event-Triggered Output Feedback Lyapunov-Based Distributed Model Predictive Control for Large-Scale Nonlinear Systems

This work presents an event-triggered (ET) output feedback Lyapunov-based distributed model predictive control (DMPC) approach for large-scale nonlinear systems. In practical applications, incomplete state information, process disturbances, and measurement noise may degrade control performance. To address these issues, a distributed extended Kalman filter (DEKF) estimator is designed to reconstruct the system states for output feedback controller design. The convergence of the designed DEKF estimator is theoretically established. Based on the estimated states, an output feedback Lyapunov-based DMPC algorithm is developed by explicitly considering the influences of the coupling subsystems to reduce the scale of the control problem. Furthermore, an event-triggering condition is derived to reduce unnecessary online optimization and communication, forming a DEKF-based ET-DMPC framework. The recursive feasibility of the proposed ET-DMPC and the stability of the closed-loop system are rigorously proved. Finally, the proposed DEKF-based ET-DMPC algorithm is applied to a nonlinear continuous stirred-tank reactor (CSTR) system. The simulation results demonstrate that the proposed method reduces the computational burden while maintaining satisfactory control performance.