Semi-explicit MPC based on subspace clustering
Semi-explicit MPC based on subspace clustering
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DOI：
10.1016/j.automatica.2017.06.036
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
G. Goebel;F. Allgöwer
中科院分区：
文献类型：
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作者：
G. Goebel;F. Allgöwer
本文提出了一种简化线性模型预测控制(MPC)在线计算的新策略。对MPC中的优化变量采用一种特定类型的状态相关参数化，将显式MPC的优点与基于在线优化的MPC的优点结合在一起，形成有效的MPC方案。对由状态和MPC优化问题的相应解组成的训练数据应用定制的子空间聚类算法，离线计算参数化。然后对其进行细化，以保证参数化优化的可行性。在离线设计阶段，可以调整参数化的复杂性，并且可以在控制性能与在线计算工作量和存储需求之间进行权衡。数值算例对所提出的方法进行了评价，并说明了它们的优点。
This paper presents a new strategy of simplifying the online computations in linear model predictive control (MPC). Employing a specific type of state dependent parameterization for the optimization variable in MPC, advantages of explicit MPC are combined with those of online optimization based MPC into an efficient MPC scheme. The parameterization is computed offline applying a tailored subspace clustering algorithm to training data consisting of states and corresponding solutions to the MPC optimization problem. It is then refined to guarantee feasibility of the parameterized optimization. During the offline design phase, complexity of the parameterization can be adjusted and control performance can be traded off against online computational effort and storage requirements. Numerical examples evaluate the presented methods and illustrate their benefits.