论文标题
使用可调管的耦合子系统的分层MPC
Hierarchical MPC for coupled subsystems using adjustable tubes
论文作者
论文摘要
为具有状态和输入约束的耦合离散时间线性系统提供了分层模型预测控制（MPC）公式。与一种集中式方法相比，一个两级层次控制器，上层中有一个控制器，每个子系统中的一个控制器都可以显着降低与MPC相关的计算成本。使用可调管实现了层次协调，该试管通过上层控制器和限制允许的下层控制器偏差与由高级控制器确定的系统轨迹进行了优化。这些可调管的大小决定了子系统之间的不确定性程度，并直接影响基于管子的强大MPC框架下所需的约束拧紧。集合表示为地位，以使能够优化这些可调管的大小并在线执行必要的约束拧紧的能力，这是MPC优化问题的一部分。状态和输入约束满意度已证明了两级层次控制器，在下层处有任意数量的控制器，数值示例证明了该方法的关键特征和性能。
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.
