一种用于扩大线性模型预测控制器吸引域的可行性调节器
A Feasibility Governor for Enlarging the Region of Attraction of Linear Model Predictive Controllers
AI总结：
针对带逐时刻约束的线性 MPC 跟踪问题，提出附加 Feasibility Governor，通过离线多面体投影与在线凸二次规划修改参考，以扩大吸引域并保证稳定、零偏差和有限时间收敛。
摘要：
本文提出一种在存在逐时刻约束的情况下，跟踪分段恒定参考时扩大线性模型预测控制器（MPC）吸引域的方法。该方法由一个附加单元——可行性调节器（FG）构成，它通过调节参考指令来确保作为 MPC 反馈律基础的最优控制问题始终保持可行。文中采用基于多目标线性规划的离线多面体投影算法来计算可行状态与参考指令集合。在线运行时，FG 的作用通过求解一个凸二次规划来计算。结果表明，闭环系统能够满足约束、渐近稳定、实现零偏差跟踪，并表现出参考的有限时间收敛。
英文摘要：
This paper 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 multi-objective 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
