In this paper, an approximation-based event-triggered model predictive control (AETMPC) strategy is proposed to implement event-triggered model predictive control for continuous-time constrained nonlinear systems under the digital platform. In the AETMPC strategy, both of the optimal control problem (OCP) and the triggering conditions are defined in a discrete-time manner based on approximate discrete-time models, while the plant under control is continuous time. In doing so, sensing load is alleviated because the triggering condition does not need to be checked continuously, and the computation of the OCP is simpler since which is calculated in the discrete-time framework. Meanwhile, robust constraints are satisfied in a continuous-time sense by taking inter-sampling behavior into consideration, and a novel constraint tightening approach is presented accordingly. Furthermore, the feasibility of the AETMPC strategy is analyzed and the associated stability of the overall system is established. Finally, this strategy is validated by a numerical example.

本文提出了一种基于近似的事件触发模型预测控制（AETMPC）策略，以在数字平台下实现连续时间约束非线性系统的事件触发模型预测控制。在AETMPC策略中，基于近似离散时间模型，以离散时间方式定义最优控制问题（OCP）和触发条件，而受控工厂是连续时间。这样，由于不需要连续检查触发条件，因此减轻了感测负载，并且由于OCP的计算是在离散时间框架中进行的，因此OCP的计算更加简单。同时，通过考虑采样间行为，可以在连续时间内满足鲁棒性约束，并据此提出了一种新颖的约束收紧方法。此外，分析了AETMPC策略的可行性，并确定了整个系统的相关稳定性。最后，通过数值例子验证了该策略。