.png){kind=icon}
返回
Deterministic and Randomized Actuator Scheduling With Guaranteed Performance Bounds
.png){kind=icon}
OA
AI
DOI：10.1109/TAC.2020.3000976.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this article, we investigate the problem of actuator selection for linear dynamical systems. We develop a framework to design a sparse actuator schedule for a given large-scale linear system with guaranteed performance bounds using deterministic polynomial-time and randomized approximately linear-time algorithms. First, we introduce systemic controllability metrics for linear dynamical systems that are monotone and homogeneous with respect to the controllability Gramian. We show that several popular and widely used optimization criteria in the literature belong to this class of controllability metrics. Our main result is to provide a polynomial-time actuator schedule that on average selects only a constant number of actuators at each time step, independent of the dimension, to furnish a guaranteed approximation of the controllability metrics in comparison to when all actuators are in use. Our results naturally apply to the dual problem of sensor selection, in which we provide a guaranteed approximation to the observability Gramian. We illustrate the effectiveness of our theoretical findings via several numerical simulations using benchmark examples.
Keyword:
Actuators
Controllability
Robot sensing systems
Observability
Schedules
Measurement
Linear systems
Approximation algorithm
complexity theory
controllability
dynamic scheduling
linear dynamical systems
sparse sensor and actuator selections
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
机构
引用论文
.png){kind=icon}
On single-phase status and segregation of an as-solidified septenary refractory high entropy alloyOAAI
OAAI分享收藏Pilot serosurvey of Coxiella burnetii in domesticated small ruminants in the United Arab Emirates
PREAI
分享收藏
Identification of Epilepsy Seizures Using Multi-resolution Analysis and Artificial Neural Networks
PREAI
分享收藏分享收藏
查看更多内容