Special Sections

Editor: Kyriakos G. Vamvoudakis
Georgia Institute of Technology
https://kyriakos.ae.gatech.edu
[email protected]

Submission Window: 15 Oct 2024 – 15 April 2025
The section will be recurrent, and a planned submission window will be open at least once a year.

Harnessing the power of machine learning to continuously monitor and detect anomalies advances the state of the art in instrumentation control. Learning-enabled systems have been rapidly increasing in size and acquiring new capabilities. These systems are typically deployed in complex operating environments, so their safety becomes extremely important. Ensuring safety requires that systems are robust to extreme events while we can monitor them for anomalous and unsafe behavior. While traditional machine learning systems are evaluated pointwise with respect to a fixed test set, such static coverage provides only limited assurance when exposed to unprecedented conditions in complex operating environments. One key question that remains unanswered is “How can we design and deploy learning-enabled systems that can be robust to extreme events while monitoring them for anomalous and unsafe behavior?” This special issue aims to contribute to this growing area of interest and thus calls for papers in this topical area.

Topics of interest for this special issue include and are not limited to:

• Machine learning for dimensionality reduction and system identification
• Emerging applications for learning-based control
• Data-driven optimization and control for dynamical systems
• Safe reinforcement learning and safe adaptive control
• Bridging model-based and learning-based control systems
• Distributed learning over distributed systems
• Reinforcement learning for multiagent systems
• Optimization, dynamics and control for machine learning
• Reinforcement learning and statistical learning for dynamical and control systems

OJ-CSYS Vol. 3
Resilient and Safe Control in Multi-Agent Systems

Multi-agent systems (MASs) have gained a lot of popularity in recent years in different disciplines as a means to solve complex tasks by subdividing them into smaller problems. The modular and interconnected structure provides important benefits, like scalability and design flexibility, but can also lead to vulnerability, by allowing local faults to spread to neighboring locations and even to the whole system. This challenge is outside the scope of robust and adaptive control, where the controller typically assumes prior knowledge about disturbances affecting the model. This is especially true for adversarial disturbances, which cannot be modeled with confidence and may fatally disrupt a control task at the global level.

Nonetheless, propagating failures are a crucial issue in several application domains, from power grids subject to cyber-attacks and power outages, to multi-robot systems dealing with unexpected changes in the surrounding environment, to vehicular networks experimenting with unpredictable behaviors of human drivers. These cases require resilient strategies that can restore the system functionalities on-the-fly in the face of unexpected or adversarial conditions that fall outside of the design assumptions.
A unifying framework for resilience in MASs is still lacking, and the complexity of large-scale systems may prevent applicability of several proposed approaches. Ultimately, the path toward resilient networked control systems is still long, and further research and technological effort is needed to cope with adversities of any kind and increasing sophistication. This special issue aims to contribute to this growing area of interest.

Guest Senior Editors
Luca Schenato, University of Padova
Ruggero Carli, University of Padova

Guest Associate Editors
Mauro Franceschelli, University of Cagliari
Hideaki Ishii, Tokyo Institute of Technology
Maryam Kamgarpour, Swiss Federal Institute of Technology Lausanne
Yancy Diaz-Mercado, University of Maryland
Chuchu Fan, MIT
Solmaz Kia, University of California, Irvine
Stephen Smith, University of Waterloo
Pavankumar Tallapragada, Indian Institute of Science
Minghui Zhu, Pennsylvania State University

OJ-CSYS Vol. 3
Control and Monitoring of Next-Gen Urban Infrastructure: Applications to Power, Transportation, and Water Systems

Climate change, overpopulation, aging infrastructure, urbanization, and the natural finiteness of earth’s resources has pushed urban designers, city planners, policy makers, scientists, and engineers to rethink traditional control and design paradigms, and to look for holistic solutions to ensure the safety, resilience, security, and efficiency of operating new infrastructure. In particular, the three key infrastructure – electric power systems, water systems, and traffic networks – all face monumental challenges related to real-time operation. To that end, this special section focuses on presenting and sharing new control algorithms and architectures for the next generation of urban infrastructure with a specific focus on power, transportation, and water systems.

Guest Senior Editor
Ahmad Taha, Vanderbilt University

Guest Associate Editors
Maria Laura Delle Monache, University of California, Berkeley
Mads Almassalkhi, University of Vermont
Christian Claudel, University of Texas at Austin
Ahmed A. Abokifa, University of Illinois Chicago
Marcio Giacomoni, University of Texas at San Antonio
Mahnoosh Alizadeh, University of California, Santa Barbara
Carlos Ocampo-Martinez, Universitat Politecnica de Catalunya

OJ-CSYS, Vol. 3
Modeling, Control, and Learning Approaches for Human-Robot Interaction Systems

Despite significant advances in robotics and autonomy, human participation is essential for the practical utilization and performance of such systems. An individual may act as a decision-maker, supervisor, or collaborator of a robot, working together to achieve a common goal. The synergy of human intelligence with robots has been shown to improve the joint human-robot system performance and reduce workload. There are many important aspects to enable effective human-robot interaction (HRI), e.g., the design of user-friendly human-machine interfaces and meta-analysis of human factors affecting robot behaviors. Among all these aspects, there is a great demand to perform system-level analysis, estimation, and prediction and provide performance guarantees for these HRI systems. The problem is challenging due to the uncertain nature of human behaviors and interactions with robots. This, therefore, calls for innovations in the modeling, control, and learning approaches and their integrations for HRI systems.

Guest Senior Editor
Yue Wang, Clemson University

Guest Associate Editors
Yancy Diaz-Mercado, University of Maryland
Victor H. Duenas, Syracuse University
Takeshi Hatanaka, Tokyo Institute of Technology
Sandra Hirche, Technische Universität München
Neera Jain, Purdue University
Meeko Oishi, University of New Mexico
Stephen L. Smith, University of Waterloo
Vaibhav Srivastava, Michigan State University
Yildiray Yildiz, Bilkent University

OJ-CSYS, Vol. 2
Synchronization in Natural and Engineering Systems

Synchronized behaviors arise spontaneously and by design in various natural and man-made systems. For instance, distinctive network-wide patterns of synchrony determine the coordinated motion of orbiting particle systems, promote successful mating in populations of fireflies, regulate the active power flow in electrical grids, and enable numerous cognitive functions in the brain. While some systems rely on synchronization of all units to function properly, other systems exhibit a rich repertoire of synchronized behaviors including cluster synchronization, chimera states, explosive synchronization patterns, and even transient, cross-frequency, and phase-amplitude synchronization. These coordinated behaviors can emerge from the properties of the interconnection structure among the units, be the result of the dynamics of the isolated units, rely on the interplay of structure and dynamics, or be driven by exogenous stimuli.

Despite being one of the most studied phenomena in science and engineering, the principles underlying general synchronization patterns in complex systems and, importantly, effective methods to regulate different forms of synchronized behaviors, have remained elusive.

Guest Senior Editor
Fabio Pasqualetti, University of California, Riverside

Guest Associate Editors
Vaibhav Srivastava, Michigan State University
ShiNung Ching, Washington University in St. Louis
Erfan Nozari, University of California, Riverside
Giovanni Russo, University of Salerno, Italy
Adilson E. Motter, Northwestern University
Zahra Aminzare, University of Iowa
Madalena Chaves, Inria Sophia Antipolis - Mediterranean
Corentin Briat, ETH-Zürich Switzerland

OJ-CSYS, Vol. 2
Formal Verification and Synthesis of Cyber-Physical Systems

CPSs are complex systems resulting from intricate interactions of computational devices with the physical plants. Recent advances in device manufacturing, computation, and storage have made tremendous advances in hardware and systems platforms for CPSs. With this growing trend in computational devices, CPSs are becoming more and more ubiquitous with many safety-critical applications including autonomous transportations, robot-assisted surgery, medical devices such as artificial pancreas, smart manufacturing, smart buildings, etc. Unfortunately, the analysis and design of CPSs nowadays are still based on ad-hoc solutions sought by simply taking the union of the classical techniques in control theory and computer science. This results in error-prone analysis or design, and very high testing and validation costs. Formal-methods based approach to CPS design recommends rigorous requirement specification in every stage of the system development. Formal verification and controller synthesis are two leading approaches to provide correctness guarantees for CPS with respect to such requirements. While formal verification aims at providing a proof of correctness with respect to the given specifications, the goal of the controller synthesis approach is more ambitious: it takes a control system together with the specification and produces a controller such that the resulting closed-loop satisfies the specification.

Guest Senior Editor
Majid Zamani, University of Colorado, Boulder

Guest Associate Editors
Samuel Coogan, Georgia Institute of Technology
Melkior Ornik, University of Illinois Urbana-Champaign
Chuchu Fan, MIT 
Ebru Aydin Gol, Middle East Technical University
Raphael Jungers, UCLouvain
Jun Liu, University of Waterloo
Meeko Oishi, University of New Mexico
Jana Tumova, KTH Royal Institute of Technology
Anne-Kathrin Schmuck, Max Planck Institute for Software Systems
Abolfazl Lavaei, Newcastle University

OJ-CSYS, Vol. 1, 2, 3
Intersection of Machine Learning with Control

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.

Vol. 3

Guest Senior Editor
Peter J. Seiler, University of Michigan

Guest Associate Editors
Neera Jain, Purdue University
Wang Gang, Beijing Institute of Technology
Carlos Ocampo-Martinez, Universitat Politècnica de Catalunya
Huazhen Fang, University of Kansas
Insoon Yang, Seoul National University
Alberto Speranzon, Honeywell Aerospace
Minghui Zhu, Pennsylvania State University
Yue Wang, Clemson University
Mahnoosh Alizadeh, University of California, Santa Barbara

Vol. 2

Guest Senior Editor
Lacra Pavel, University of Toronto

Guest Associate Editors
Neera Jain, Purdue University
Alberto Speranzon, Lockheed Martin
Wang Gang, Beijing Institute of Technology
Minghui Zhu, Pennsylvania State University
Somayeh Sojoudi, University of California, Berkeley
Yue Wang, Clemson University
Peter Seiler, University of Michigan
Mahnoosh Alizadeh, University of California, Santa Barbara
Insoon Yang, Seoul National University

Vol. 1

Senior Editor
Sonia Martinez, University of California, San Diego

Guest Associate Editors
Neera Jain, Purdue University
Massimo Canale, Politecnico di Torino, Turin
Somayeh Sojoudi, University of California, Berkeley
Peter Seiler, University of Michigan
Huazhen Fang, University of Kansas
Insoon Yang, Seoul National University
Alberto Speranzon, Lockheed Martin
Minghui Zhu, Pennsylvania State University
Yue Wang, Clemson University
Mahnoosh Alizadeh, University of California, Santa Barbara