Get Published in the inaugural issue of IEEE’s Open Journal of Control Systems
Estimated twenty-two-week review process, starting at manuscript submission
Special Offer: APCs waived for the first 20 accepted papers
No overlength charges
The IEEE Open Journal of Control Systems is a new publication of the IEEE Control Systems Society. The journal aims to publish high-quality papers on the theory, design, optimization, and applications of dynamic systems and control. The Editorial Board on the back of this brochure demonstrates the breadth of areas covered within the journal. The journal’s main mission is the promotion of open access to all control systems research and education publications, including software, and data. Some journal highlights include:
Special emphasis areas are the interplay between data science and control, and the interdisciplinary connection of dynamic systems and controls with diverse applications in biological, social, cognitive, and cyber-physical systems.
New publication categories will be featured in addition to regular papers, including overview, position, open software/ testbed tutorial papers, and reproducible papers in dynamic systems and controls.
Upcoming Special Sections and Issues covering topics such as Brain networks, and Human-Robot Interaction will be announced soon. Special sections such as the one below are not exclusive of other areas in control.
Keywords:
Adaptive systems
Agents and Autonomous systems
Cognitive systems and their control
Communication networks
Biomolecular systems
Computational methods
Decision theory
Delay systems
Embedded systems
Finance, economic systems
Formal verification/verification/synthesis
Healthcare and medical systems
Human in the loop systems
Information theory and control
Machine Learning and control
Mechatronics
Network analysis and control
Systems analysis, stability, and control
Optimization
Resilient Control Systems
Robotics
Smart grids
Social networks
Stochastic systems
Systems Biology
Special Section (Deadline March 31, 2022 April 30, 2022): Unprecedented technological advances have fueled the creation of devices that can collect, generate, store, and transfer large amounts of data. This massive, data outpour is profoundly changing the way in which complex engineering problems are solved, calling for the conception of new interdisciplinary tools at the intersection of machine learning, dynamic systems and control, and optimization. While the repurposing of control theories building on new Machine Learning methods can be highly successful, Dynamic Systems and Control can greatly contribute to analyze and devise novel adaptive, safety-critical controllers with performance guarantees. This special issue aims to contribute to this growing area of interest, and calls thus for papers in this topical area.
ARTICLE TYPES
• Regular Papers are standard journal articles, presenting significant research on analysis relevant for dynamic control systems, and/or applications. These papers can extend work presented at a previous conference.
• Overview Papers should provide an introduction to the topic being surveyed, starting with key definitions and statements that allow a reader to get a clear grasp of the fundamental components of the area being reviewed. Authors should provide an overview of techniques applied to address main questions in the area, describing the pros-cons of an important subset of competing techniques with the goal of finding the limits to the state of the art.
• Position/Outlook Papers are short papers presenting future challenges and new developments in dynamic systems and controls. Papers can outline key challenges, provide guidelines, and future agendas for the field, or contextualize findings adding a new dimension to the Dynamic Systems and Control field. Alternatively, they can identify technical bottlenecks in current research, its open problems, and new techniques to solve them.
• Testbeds/Software/Data Papers are novel tutorial-like papers that describe available testbeds, open-source software developed and shared by authors, open-source data sets that can be used by others for benchmarking tests, or report on research case studies of practical relevance.
Questions? Reach out to contact the Editorial Assistant at var003@eng.ucsd.edu
