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In this paper, 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.
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Know your Enemy: Benchmarking and Experimenting with Insight as a Goal
Available benchmark suites are used to provide realistic workloads and to understand their run-time characteristics. However, they do not necessarily target the same platforms and often offer a diverse set of metrics, leading to the lack of a knowledge base that could be used for both systems and theoretical research. RT-Bench, a new benchmark framework environment, tries to address these issues by providing a uniform interface and metrics while maintaining portability. This demo illustrates how to leverage this framework and its recently added features to improve the understanding of the benchmarks’ interaction with its system.
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- Award ID(s):
- 2008799
- PAR ID:
- 10482042
- Publisher / Repository:
- RTSS@Work 2022
- Date Published:
- Journal Name:
- WiP Session @ 43rd IEEE Real-Time Systems Symposium (RTSS@Work 2022)
- Format(s):
- Medium: X
- Location:
- Houston, TX, USA
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
