Current Special Sections Resilient and Safe Control in Multi-Agent Systems Control and Monitoring of Next-Gen Urban Infrastructure: Applications to Power, Transportation, and Water Systems Intersection of Machine Learning with Control Resilient and Safe Control in Multi-Agent Systems Editors: Luca Schenato University of Padova Italy http://automatica2.dei.unipd.it/people/schenato.html [email protected] Ruggero Carli University of Padova Italy [email protected] Submission Window: 1 Oct 2023 - 1 April 2024 Extended to 30 April 2024 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 and calls thus for papers in this topical area. Topics of interest for this special issue include and are not limited to: Resilient-by-design architectures and formal verification Reactive resilience architectures and methodologies Data-driven and learning-based resilience control Self-organizing control architectures Resilient multi-agent optimization and federated learning Resilient multi-robot systems Safety-critical control Fault-detection and isolation for networked systems Game-theoretic approaches to resilient control Resilient multi-agent estimation in sensor networks Anomaly-detection/malicious agents detention and mitigation Privacy-preserving distributed algorithms Control and Monitoring of Next-Gen Urban Infrastructure: Applications to Power, Transportation, and Water Systems Editor: Ahmad Taha Vanderbilt University United States https://lab.vanderbilt.edu/taha/ [email protected] Submission Window: 15 Oct 2023 – 15 April 2024 30 April 2024 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. The scope of each of these infrastructures is defined within the list of topics below. Topics of interest for this special issue include and are not limited to: Power systems: Transmission and distribution systems Operation and feedback control problems State estimation and monitoring algorithms Control-theoretic cyber-security problems Decarbonization of power systems and climate change mitigation Control/monitoring of low-carbon power systems Learning algorithms for power systems modeling and control Transportation systems: Connected and autonomous vehicles control problems Traffic control and prediction Control-theoretic cyber-security methods Learning algorithms for transportation systems modeling and control State estimation of advanced traffic dynamics Water systems: Drinking water distribution networks Stormwater and urban drainage systems Flood control systems Hydraulics and water quality Feedback control algorithms State estimation and calibration methods Learning algorithms for water systems Smart sensing and control Digital twins for water systems Cyber-physical security of water infrastructure Multi-infrastructure problems: Water-energy nexus and joint control of water-power networks Electrification of transportation systems Joint control of electrified transportation and power distribution networks Intersection of Machine Learning with Control (Recurring) Editor: Peter J. Seiler University of Michigan https://seiler.engin.umich.edu [email protected] Submission Window: 15 Nov 2023 – 30 April 2024 The section will be recurrent, and a planned submission window will be open at least once a year. 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. 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 Please contact the OJ-CSYS editorial assistant with any questions. Special Section on Resilient and Safe Control in Multi-Agent Systems Download Special Section on Control and Monitoring of Next-Gen Urban Infrastructure: Applications to Power, Transportation, and Water Systems Download Special Section on Intersection of Machine Learning with Control Download
