论文标题
分布式模型预测控制，具有不对称自适应终端集，用于调节大型系统
Distributed Model Predictive Control with Asymmetric Adaptive Terminal Sets for the Regulation of Large-scale Systems
论文作者
论文摘要
在本文中，开发了一种具有非对称自适应终端集的新型分布式模型预测控制（MPC）方案，以调节具有分布式结构的大规模系统。与典型的MPC方案相似，依靠系统的分布式结构的结构化Lyapunov矩阵和分布式终端控制器是离线计算的。但是，在此方案中，在线计算一个分布式不变的终端集，并在每次即时考虑系统的当前状态时进行更新。特别是，我们认为椭圆形终端集很容易计算大型系统。这些终端集的大小和中心以及预测的状态和输入轨迹被认为是在线阶段的决策变量。发现在线确定终端集中中心是在存在不对称约束的情况下特别有用的。最后，放宽了由此产生的在线最佳控制问题。通过将其与自适应终端集进行比较，在模拟中将提出方案的功效进行了比较。
In this paper, a novel distributed model predictive control (MPC) scheme with asymmetric adaptive terminal sets is developed for the regulation of large-scale systems with a distributed structure. Similar to typical MPC schemes, a structured Lyapunov matrix and a distributed terminal controller, respecting the distributed structure of the system, are computed offline. However, in this scheme, a distributed positively invariant terminal set is computed online and updated at each time instant taking into consideration the current state of the system. In particular, we consider ellipsoidal terminal sets as they are easy to compute for large-scale systems. The size and the center of these terminal sets, together with the predicted state and input trajectories, are considered as decision variables in the online phase. Determining the terminal set center online is found to be useful specifically in the presence of asymmetric constraints. Finally, a relaxation of the resulting online optimal control problem is provided. The efficacy of the proposed scheme is illustrated in simulation by comparing it to a recent distributed MPC scheme with adaptive terminal sets.
