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IEEE Open Journal of Control Systems is a peer-reviewed journal published by IEEE, covering Advanced Control Systems Optimization, Control Systems and Identification, Reinforcement Learning in Robotics. Over its lifetime it has published 190 papers, which have received 654 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 Open Journal of Control Systems, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Open Journal of Control Systems style. No sign-up. |
| Publishes research in | Advanced Control Systems Optimization Control Systems and Identification Reinforcement Learning in Robotics Fault Detection and Control Systems Smart Grid Security and Resilience |
| ISSN | 2694-085X |
| Citation impact (2-yr) | 1.27 |
| h-index | 14 |
| i10-index | 24 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | University of California San Diego |
| 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.
While reinforcement learning produces very promising results for many applications, its main disadvantage is the lack of safety guarantees, which prevents its use in safety-critical systems. In this work, we address this issue by a safety shield for nonlinear continuous systems that solve reach-avoid tasks. Our safety shield prevents applying potentially unsafe actions from a…
This paper considers distributed allocation strategies, formulated as a distributed sum-preserving (fixed-sum) allocation of resources over a multi-agent network in the presence of heterogeneous arbitrary time-varying delays. We propose a double time-scale scenario for unknown delays and a faster single time-scale scenario for known delays. Further, the links among the nodes are considered subject to…
Task-specific, trajectory-based control methods commonly used in exoskeletons may be appropriate for individuals with paraplegia, but they overly constrain the volitional motion of individuals with remnant voluntary ability (representing a far larger population). Human-exoskeleton systems can be represented in the form of the Euler-Lagrange equations or, equivalently, the port-controlled Hamiltonian equations to design control laws…
Developing model predictive control (MPC) schemes can be challenging for systems where an accurate model is not available, or too costly to develop. With the increasing availability of data and tools to treat them, learning-based MPC has of late attracted wide attention. It has recently been shown that adapting not only the MPC model, but…
Trust model is a topic that first gained interest in organizational studies and then human factors in automation. Thanks to recent advances in human-robot interaction (HRI) and human-autonomy teaming, human trust in robots has gained growing interest among researchers and practitioners. This article focuses on a survey of computational models of human-robot trust and their…
