This paper is concerned with the resilient dynamic output-feedback (DOF) distributed model predictive control (DMPC) problem for discrete-time polytopic uncertain systems under synchronous Round-Robin (RR) scheduling. In order to alleviate the computation burden and improve the system robustness against uncertainties, the global system is decomposed into several subsystems, where each subsystem under synchronous RR scheduling communicates with each other via a network. The RR scheduling is adopted to avoid data collisions, however the updating information at each time instant is unfortunately reduced, and the underlying RR scheduling of subsystems are deeply coupled. The main purpose of this paper is to design a set of resilient DOF-based DMPC controllers for systems under the consideration of polytopic uncertainties and synchronous RR scheduling, such that the desirable performance can be obtained at a low cost of computational time. A novel distributed performance index dependent of the synchronous RR scheduling is constructed, where the last iteration information from the neighbor subsystems is used to deal with various couplings. Then, by resorting to the distributed RR-dependent Lyapunov-like approach and inequality analysis technique, a certain upper bound of the objective is put forward to establish a solvable auxiliary optimization problem (AOP). Moreover, by using the Jacobi iteration algorithm to solve such a problem online, the distributed feedback gains are directly obtained to guarantee the convergence of system states. Finally, two examples including a distillation process example and a numerical example are employed to show the effectiveness of the proposed resilient DMPC strategy.

本文关注的是基于同步轮循（RR）调度的离散时间多目标不确定系统的弹性动态输出反馈（DOF）分布式模型预测控制（DMPC）问题。为了减轻计算负担并提高系统对不确定性的鲁棒性，将全局系统分解为几个子系统，其中在同步RR调度下的每个子系统都通过网络相互通信。虽然采用了RR调度来避免数据冲突，但是不幸的是，每个时刻的更新信息都减少了，子系统的基础RR调度也被深深地耦合了。本文的主要目的是为系统设计一套基于弹性DOF的DMPC控制器，该系统考虑了多态性不确定性和同步RR调度，这样就可以以较低的计算时间获得所需的性能。构造了一种依赖于同步RR调度的新型分布式性能指标，其中使用了来自相邻子系统的最新迭代信息来处理各种耦合。然后，通过依赖于分布RR的类Lyapunov样方法和不等式分析技术，提出了目标的一定上限，以建立可解决的辅助优化问题。此外，通过使用雅可比（Jacobi）迭代算法在线解决该问题，可以直接获得分布式反馈增益，以保证系统状态的收敛。最后，