使用可调管的耦合子系统的分层MPC
Hierarchical MPC for coupled subsystems using adjustable tubes
摘要：
针对具有状态和输入约束的耦合离散线性系统，提出了一种递阶模型预测控制（MPC）方法。与集中式方法相比，两级分层控制器（在上层中具有一个控制器并且在下层中每个子系统具有一个控制器）可以显著降低与MPC相关联的计算成本。分层协调是使用可调管，这是优化的上层控制器和边界允许的较低级别的控制器从系统轨迹确定的上层控制器的偏差。这些可调管的大小决定了子系统之间的不确定性程度，并直接影响基于管的鲁棒MPC框架下所需的约束收紧。集合被表示为zonotopes，以使得能够优化这些可调节管的尺寸，并作为MPC优化问题的一部分在线执行必要的约束收紧。状态和输入约束满足证明了两级分层控制器与任意数量的控制器在较低的水平和一个数值例子演示的关键功能和性能的方法。
英文摘要：
A hierarchical Model Predictive Control (MPC) formulation is presented for coupled discrete-time linear systems with state and input constraints. Compared to a centralized approach, a two-level hierarchical controller, with one controller in the upper-level and one controller per subsystem in the lower-level, can significantly reduce the computational cost associated with MPC. Hierarchical coordination is achieved using adjustable tubes, which are optimized by the upper-level controller and bound permissible lower-level controller deviations from the system trajectories determined by the upper-level controller. The size of these adjustable tubes determines the degree of uncertainty between subsystems and directly affects the required constraint tightening under a tube-based robust MPC framework. Sets are represented as zonotopes to enable the ability to optimize the size of these adjustable tubes and perform the necessary constraint tightening online as part of the MPC optimization problems. State and input constraint satisfaction is proven for the two-level hierarchical controller with an arbitrary number of controllers at the lower-level and a numerical example demonstrates the key features and performance of the approach.
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