We propose a novel adaptive learning-based model predictive control (MPC) scheme for interconnected systems which can be decomposed into several smaller dynamically coupled subsystems with uncertain coupling. The proposed scheme is mainly divided into two main online phases; a learning phase and an adaptation phase. Set membership identification is used in the learning phase to learn an uncertainty set that contains the coupling strength using online data. In the adaptation phase, rigid tube-based robust MPC is used to compute the optimal predicted states and inputs. Besides computing the optimal trajectories, the MPC ingredients are adapted in the adaptation phase taking the learnt uncertainty set into account. These MPC ingredients include the prestabilizing controller, the rigid tube, the tightened constraints and the terminal ingredients. The recursive feasibility of the proposed scheme as well as the stability of the corresponding closed-loop system are discussed. The developed scheme is compared in simulations to existing schemes including robust, adaptive and learning-based MPC.

中文翻译：

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基于自适应学习的不确定互联系统模型预测控制：一种集合成员身份识别方法


我们为互连系统提出了一种新的基于自适应学习的模型预测控制 （MPC） 方案，该方案可以分解为几个具有不确定耦合的较小动态耦合子系统。拟议的计划主要分为两个主要的上线阶段;学习阶段和适应阶段。在学习阶段使用集合成员身份识别来学习使用在线数据包含耦合强度的不确定性集。在适应阶段，使用基于刚性管的稳健 MPC 来计算最佳预测状态和输入。除了计算最佳轨迹外，MPC 成分在适应阶段还考虑了学习的不确定性。这些 MPC 成分包括预稳定控制器、刚性管、紧固约束和终端成分。讨论了所提方案的递归可行性以及相应闭环系统的稳定性。在仿真中将开发的方案与现有方案（包括稳健、自适应和基于学习的 MPC）进行了比较。