Hierarchical MPC for coupled subsystems using adjustable tubes
Hierarchical MPC for coupled subsystems using adjustable tubes
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DOI：
10.1016/j.automatica.2022.110435
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Vignesh Raghuraman;Justin P. Koeln
中科院分区：
文献类型：
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作者：
Vignesh Raghuraman;Justin P. Koeln
针对具有状态约束和输入约束的耦合离散线性系统，提出了一种层次模型预测控制（MPC）方法。与集中式方法相比，两级分层控制器（上层一个控制器，下层每个子系统一个控制器）可以显著降低与MPC相关的计算成本。层次协调是通过可调管来实现的，可调管由上层控制器进行优化，并限制允许的下层控制器与上层控制器确定的系统轨迹的偏差。这些可调节管的尺寸决定了子系统之间的不确定性程度，并直接影响到基于管的鲁棒MPC框架所需的约束拧紧程度。作为MPC优化问题的一部分，集管被表示为分区，以便能够优化这些可调节管的尺寸，并在线执行必要的约束拧紧。证明了具有任意数量下一级控制器的两级层次控制器的状态和输入约束满足性，并通过数值算例说明了该方法的主要特点和性能。
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.