A Feasibility Governor for Enlarging the Region of Attraction of Linear Model Predictive Controllers
A Feasibility Governor for Enlarging the Region of Attraction of Linear Model Predictive Controllers
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DOI：
10.1109/tac.2021.3123224
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发表时间：
2020-11
影响因子：
6.8
通讯作者：
Terrence Skibik;Dominic Liao-McPherson;T. Cunis;I. Kolmanovsky;M. Nicotra
中科院分区：
文献类型：
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作者：
Terrence Skibik;Dominic Liao-McPherson;T. Cunis;I. Kolmanovsky;M. Nicotra
本文提出了一种方法，扩大吸引域的线性模型预测控制器（MPC）跟踪分段常数参考存在逐点的时间约束。它包括一个附加单元，可行性总督（FG），操纵的参考命令，以确保最优控制问题的基础MPC反馈法仍然是可行的。采用基于多目标线性规划的离线多面体投影算法计算可行状态和参考指令集。在线，FG的行动是通过求解凸二次规划计算。闭环系统满足约束条件，是渐近稳定的，表现出零偏移跟踪，并显示有限时间收敛的参考。
This article proposes a method for enlarging the region of attraction of linear model predictive controllers (MPC) when tracking piecewise-constant references in the presence of pointwise-in-time constraints. It consists of an add-on unit, the feasibility governor (FG), that manipulates the reference command so as to ensure that the optimal control problem that underlies the MPC-feedback law remains feasible. Offline polyhedral projection algorithms based on multiobjective linear programming are employed to compute the set of feasible states and reference commands. Online, the action of the FG is computed by solving a convex quadratic program. The closed-loop system is shown to satisfy constraints, be asymptotically stable, exhibit zero-offset tracking, and display finite-time convergence of the reference.