The event-triggered model predictive control (MPC) problem is addressed for polytopic uncertain systems. A new dynamic event-triggered mechanism (DETM) with a bounded dynamic variable and a time-varying threshold is proposed to manage measurement data packet releases. The dynamic output-feedback MPC issue is detailed as a “min–max” optimization problem (OP) with an objective function over an infinite horizon, where the hard constraint on the predictive control is required. By applying a Lyapunov-like function containing the bounded dynamic variable, an auxiliary OP constrained by several matrix inequalities is proposed, and the design methods of the output-feedback gains are provided if this auxiliary OP is feasible. The designed MPC controller ensures that the closed-loop system is input-to-state practically stable. Two examples including an event-triggered DC motor are given to illustrate the validity of the developed MPC algorithm. Simulation results verify that the proposed DETM has advantages over some existing triggering mechanisms in decreasing the consumption of resources while meeting the required performance.

中文翻译：

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网络化输出反馈 MPC：有界动态变量和时变阈值相关的基于事件的方法


针对多面不确定系统解决了事件触发模型预测控制（MPC）问题。提出了一种具有有界动态变量和时变阈值的新动态事件触发机制（DETM）来管理测量数据包的释放。动态输出反馈 MPC 问题被详细描述为具有无限范围目标函数的“最小-最大”优化问题 (OP)，其中需要对预测控制进行硬约束。通过应用包含有界动态变量的类Lyapunov函数，提出了受多个矩阵不等式约束的辅助OP，并在该辅助OP可行的情况下给出了输出反馈增益的设计方法。设计的MPC控制器确保闭环系统的输入到状态实际上是稳定的。给出了两个包括事件触发直流电机的例子来说明所开发的 MPC 算法的有效性。仿真结果验证了所提出的DETM在满足性能要求的同时减少资源消耗方面比现有的一些触发机制具有优势。