模型预测控制(MPC)广泛应用于过程工业，虽然MPC擅长处理多输入多输出的受约束控制问题，但滚动优化的执行方式常常需要较大的计算量和通信量，这也限制了MPC的进一步使用和推广。针对这个问题，本项目拟研究基于事件驱动的MPC策略，针对MPC执行时的多个环节提出相应的事件驱动型设计方案。这些环节主要包括优化问题的表述以及滚动优化所涉及的信息采样和在线优化等。其中，针对优化问题的表述，项目拟提出事件驱动型预测时长和事件驱动型终端约束这两种设计方案，其目的是降低优化问题的复杂度以减少在线计算量；针对滚动优化中的信息采样和在线优化，重点研究采样由时间驱动而优化由事件驱动的策略，以及采样和优化均由事件驱动的策略，旨在进一步减少在线执行所需的计算量和通信量。此外，项目将对所提出的各类事件驱动型MPC方案进行有效性分析和仿真验证，为其应用与推广提供理论支持。

Model predictive control (MPC) has been widely applied in various process industries. It is well known that MPC is capable of tackling the control problems in multiple-input-multiple-output systems with constraints. However, a considerable amount of computation and communication are involved due to the fashion of receding horizon optimization, which limits the wider use of MPC. To this end, this project will systematically investigate MPC approaches that are based on event-driven mechanisms. The designs of event-driven MPC strategies will be carried out according to several key steps, including formulating the optimal control problem (OCP), sampling the system information, as well as solving the optimization problem. This project will mainly consider the event-driven prediction horizon and the event-driven terminal constraint with respect to the formulation of OCP, which aim to reduce the complexity of the optimization problem to be solved online. Besides, two classes of event-driven strategies will be proposed corresponding to the sampling and the optimization procedures, i.e., the strategies involving the time-driven sampling as well as the event-driven optimization, and the strategies where both sampling and optimization are driven through event mechanisms. These two classes of strategies are designed in order to further reduce the cost caused by computations and communications. Moreover, feasibility analysis and numerical verifications will be carried out in this project, which help to provide guidance to the implementation of event-driven MPC in practical applications.