Regret Analysis of Shrinking Horizon Model Predictive Control

被引：0

作者：

Ambrosino, Michele ^{[1
]}

Castroviejo-Fernandez, Miguel ^{[1
]}

Leung, Jordan ^{[1
]}

Kolmanovsky, Ilya ^{[1
]}

机构：

[1] Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA

来源：

JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME | 2025年 / 147卷 / 02期

关键词：

model predictive control; constrained control; regret analysis; numerical methods for optimal control; STABILITY;

D O I：

10.1115/1.4066317

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper analyzes the suboptimal implementation of shrinking horizon model predictive control (SHMPC) when a fixed number of solver iterations and a warm-start are utilized at each time-step to solve the underlying optimal control problem (OCP). We derive bounds on the loss of performance (regret) and on the difference between suboptimal SHMPC and optimal solutions. This analysis provides insights and practical guidelines for the implementation of SHMPC under computational limitations. A numerical example of axisymmetric spacecraft spin stabilization is reported. The suboptimal implementation of SHMPC is shown to be capable of steering the system from an initial state into a known terminal set while satisfying control constraints.

引用

页数：8

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