Dual Event-Triggered Spatial Model Predictive Control for Distributed Thermal Processes.

During the distributed thermal process, frequent model updates (MUs) and controller activations can lead to worse performance due to over-computation. To address this problem, a dual event-triggered spatial model predictive control (DET-SMPC) under a data-driven framework is investigated for distributed thermal processes to achieve good global performance. The spatiotemporal model is built utilizing the time/space theorem and updated to accommodate the time-varying system dynamics. Since the controller effect will be affected when the model is switched, it is necessary to identify the preferable switching mode. Therefore, an adaptive MU approach based on an error-triggered generator is proposed. Subsequently, ET-model predictive control (MPC), the controller activation threshold derived from the Lyapunov function, is introduced. The controller will only be activated when the threshold is triggered, resulting in better performance. The availability of the dual event-triggered spatial MPC (DET-SMPC) is confirmed through both simulation studies and oven experiments.