Dual Event-Triggered Spatial Model Predictive Control for Distributed Thermal Processes
针对分布式热过程中频繁模型更新和控制器激活导致性能下降的问题，提出一种数据驱动框架下的双事件触发空间模型预测控制方法，通过自适应模型更新和基于Lyapunov函数的控制器激活阈值来提升全局性能，仿真和实验验证了有效性。
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.