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IEEE Control Systems Letters is a peer-reviewed journal published by IEEE, covering Advanced Control Systems Optimization, Stability and Control of Uncertain Systems, Control Systems and Identification. Over its lifetime it has published 3,756 papers, which have received 31,004 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Control Systems Letters, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Control Systems Letters style. No sign-up. |
| Publishes research in | Advanced Control Systems Optimization Stability and Control of Uncertain Systems Control Systems and Identification Fault Detection and Control Systems Distributed Control Multi-Agent Systems |
| ISSN | 2475-1456 |
| Citation impact (2-yr) | 1.25 |
| h-index | 58 |
| i10-index | 946 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | ieeexplore.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
The need for computationally-efficient control methods of dynamical systems under temporal logic tasks has recently become more apparent. Existing methods are computationally demanding and hence often not applicable in practice. Especially with respect to multi-robot systems, these methods do not scale computationally. In this letter, we propose a framework that is based on control barrier…
As multi-agent systems become more wide-spread and versatile, the ability to satisfy multiple system-level constraints grows increasingly important. In applications ranging from automated cruise control to safety in robot swarms, barrier functions have emerged as a tool to provably meet such constraints by guaranteeing forward invariance of desirable sets. However, satisfying multiple constraints typically implies…
This letter presents a new notion of input-to-state safe control barrier functions (ISSf-CBFs), which ensure safety of nonlinear dynamical systems under input disturbances. Similar to how safety conditions are specified in terms of forward invariance of a set, input-to-state safety conditions are specified in terms of forward invariance of a slightly larger set. In this…
A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction. The framework can be used for a wide class of nonlinear systems. Any standard supervised learning technique (e.g., neural networks) can be employed to approximate the MPC from samples. In order…
In this letter, we study distributed optimization, where a network of agents, abstracted as a directed graph, collaborates to minimize the average of locally known convex functions. Most of the existing approaches over directed graphs are based on push-sum (type) techniques, which use an independent algorithm to asymptotically learn either the left or right eigenvector…
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