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Resource-Aware Stochastic Self-Triggered Model Predictive Control

delete2022-01-01
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OA
AI
Y
Yingzhao Lian
Y
Yuning Jiang *
N
Naomi Stricker
L
Lothar Thiele
C
Colin N. Jones
DOI:10.1109/LCSYS.2021.3091967delete
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Abstract

Abstract

En 中文
This letter considers the control of uncertain systems operated under limited resource factors, such as battery life or hardware longevity. We consider here resource-aware self-triggered control techniques that schedule system operation non-uniformly in time in order to balance performance against resource consumption. When running in an uncertain environment, unknown disturbances may deteriorate system performance by acting adversarially against the planned event triggering schedule. In this work, we propose a resource-aware stochastic predictive control scheme to tackle this challenge, where a novel zero-order hold feedback control scheme is proposed to accommodate a time-inhomogeneous predictive control update.
Keywords:
Feedback control
Stochastic processes
Predictive control
Mathematical model
Dynamic scheduling
Uncertainty
Optimization
Stochastic optimal control
self-triggered model predictive control
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

I
IEEE Control Systems Letters
IF:
2
Papers:
94
Citations:
5.0K

Organization

E
Ecole Polytechnique Federale de Lausanne
Scholars:
1.7W
Papers: 1.3W
Citations: 25
S
swiss federal institutes of technology domain
Scholars:
9.0W
Papers: 8.0W
Citations: 163
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