Constraint-adaptive MPC for large-scale systems: Satisfying state constraints without imposing them

被引:4
作者
Nouwens, S. A. N. [1 ]
de Jager, B. [1 ]
Paulides, M. [2 ,3 ]
Heemels, W. P. M. H. [1 ]
机构
[1] Eindhoven Univ Technol, Mech Engn Control Syst Technol, Eindhoven, Netherlands
[2] Eindhoven Univ Technol, Elect Engn Electromagnet Care Cure Lab, Eindhoven, Netherlands
[3] Erasmus MC, Inst Canc, Dept Radiotherapy, Rotterdam, Netherlands
关键词
Model Predictive Control; Large-Scale Systems; Adaptive Constraints; MODEL-PREDICTIVE CONTROL;
D O I
10.1016/j.ifacol.2021.08.550
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Model Predictive Control (MPC) is a successful control methodology, which is applied to increasingly complex systems. However, real-time feasibility of MPC can be challenging for complex systems, certainly when an (extremely) large number of constraints have to be adhered to. For such scenarios with a large number of state constraints, this paper proposes two novel MPC schemes for general nonlinear systems, which we call constraint-adaptive MPC. These novel schemes dynamically select at each time step a (varying) set of constraints that are included in the on-line optimization problem. Carefully selecting the included constraints can significantly reduce, as we will demonstrate, the computational complexity with often only a slight impact on the closed-loop performance. Although not all (state) constraints are imposed in the on-line optimization, the schemes still guarantee recursive feasibility and constraint satisfaction. A numerical case study illustrates the proposed MPC schemes and demonstrates the achieved computation time improvements exceeding two orders of magnitude without loss of performance. Copyright (C) 2021 The Authors.
引用
收藏
页码:232 / 237
页数:6
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