本项目针对模型参数未知的线性系统，设计带有事件触发律的数据驱动预测控制方法，在保证系统稳定性及控制效果的基础上，利用事件触发律代替传统的滚动优化过程，显著减少算法的数据传输量与计算量，降低其实现代价。目前的事件触发律大多是基于已知模型进行设计的，而如何仅通过系统可测数据设计相应的事件触发律是一个较难的问题，且尚未开展有效的研究。因此，本项目首先分别在系统状态可测和不可测的情况下，结合输入稳定性条件，仅利用系统可测数据设计数据驱动预测控制的事件触发律；其次，针对实际系统中带有的约束条件，深入研究约束条件对事件触发条件及触发频率的影响，综合设计带有约束的事件触发数据驱动预测控制方法；最后，将设计的方法应用到化工过程实验平台上进行应用及仿真，验证所设计方法的效果和优势。该项目对数据驱动控制的理论发展及工程实现都具有重要的意义。

This project proposes to design the event-triggered data-driven predictive control method for the linear systems with unknown model parameters. With the ensured system stability and control performance, the proposed method substitutes the traditional receding horizontal optimization with the event-triggered law, which can reduce the data transmission and computation load significantly and facilitate the industrial application. Most of the current event-triggered laws are designed based on the prerequisite system model, however, how to develop the corresponding event-triggered law only with the system measurable data is a difficult problem and has not been studied effectively. Therefore, this project first develops the event-triggered laws for the systems with measurable and immeasurable system states respectively, based on the input-to-state stability condition. Second, with the study of the event-triggering condition and frequency in relation to the constraint conditions in practical systems, the comprehensive event-triggered data-driven predictive control method is developed under the practical system constraints. Finally, the effects and advantages of the proposed method are verified by applying the designed method to the chemical engineering platform for application and simulation. This project plays an important role for both the theoretical development and industrial application in the data-driven control domain.