A Sparsity Approach to Scheduling and Control of Networked Systems

We study the co-design of scheduling logic and control logic for networked control systems (NCSs) where plants communicate with their remotely located controllers over a shared band-limited communication network. Our key contribution is a new algorithm that co-designs (a) an allocation scheme of the communication network among the plants (scheduling logic) and (b) the control inputs for the plants accessing the network (control logic) under which given non-zero initial states are steered to zero in a given time horizon for all the plants in the NCS. Sparse optimization is the primary apparatus for our analysis. We also provide sufficient conditions on the plant dynamics, capacity of the communication network and the given time horizon that lead to a numerically tractable implementation of our algorithm. Numerical experiments are presented to demonstrate the proposed results.

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Publication Details

Journal
IEEE Transactions on Automatic Control
Published
2025-05-20
DOI
https://doi.org/10.1109/tac.2025.3571664
Primary Topic
Petri Nets in System Modeling
Type
article
Field-Weighted Citation Impact
0.00
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article

A Sparsity Approach to Scheduling and Control of Networked Systems

Anubhab Dasgupta, Atreyee Kundu
IEEE Transactions on Automatic Control
Petri Nets in System Modeling
article

A Sparsity Approach to Scheduling and Control of Networked Systems

Anubhab Dasgupta, Atreyee Kundu
article en

Abstract

We study the co-design of scheduling logic and control logic for networked control systems (NCSs) where plants communicate with their remotely located controllers over a shared band-limited communication network. Our key contribution is a new algorithm that co-designs (a) an allocation scheme of the communication network among the plants (scheduling logic) and (b) the control inputs for the plants accessing the network (control logic) under which given non-zero initial states are steered to zero in a given time horizon for all the plants in the NCS. Sparse optimization is the primary apparatus for our analysis. We also provide sufficient conditions on the plant dynamics, capacity of the communication network and the given time horizon that lead to a numerically tractable implementation of our algorithm. Numerical experiments are presented to demonstrate the proposed results.

IEEE Transactions on Automatic ControlVol. 70(10)
Indian Institute of Technology Kharagpur (IN)
Openalex Percentile: Top 88%
Petri Nets in System Modeling
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