Info_PRIN

The project aims at providing methodological advancements on a class of distributed optimization-based control and supervisory design methodologies referred to in the literature as Economical Model Predictive Control and Command Governors, which have been proved to be suitable for addressing constrained supervisory and control problems for Cyber-Physical Systems (CPSs). These advancements will concern especially the resilience of the Optimization Algorithms underlying the online command computation with respect to adversarial behaviors and, more in general, to uncertainty in the shared data arising naturally for different causes in the distributed context of CPSs. The latter system paradigm, where cyber and physical aspects coexist and influence each other, has received in the last years increasing interest from the international control community, for its ability to describe complex phenomena arising in modern engineering systems, applications and services as large-scale industrial plants, power grids, water distribution infrastructures and intelligent transportation systems to name a few, collectively giving rise to the so-called Smart-World.

All these complex systems have, as a common characteristic, the fact that they consist of a variety of spatially distributed interactive nodes representing sensors, actuators and subsystems, possibly dynamically coupled and connected via communication links, which need to be controlled and coordinated in order to accomplish their overall objective. The evolutions of the subsystems are typically subjected to safety, operative and coordination constraints that usually take the form of pointwise-in-time set-membership constraints on relevant variables of the subsystems, to be fulfilled during the CPS evolutions. The communication network is typically subject to latency, which is usually modeled abstractly as a time-varying time delay, and also packet dropouts may result in some adverse situations that make the distributed optimization and control problems challenging. Moreover, adversarial behaviors can disturb the systems. 

Amongst many, the one of main interest here is referred to in the literature as non-compliance. In particular, in the case of cooperative optimization, this might happen when one or more agents in the network do not cooperate because they selfishly want to have better performance. In other words, the adversarial agents might optimize a local cost with the goal to steer the iterates to a point that serves their own interest despite possible global performance worsening. Thus, the properties of the cooperative distributed schemes in supervising and coordinating the nodes of a constrained CPS will be investigated and, in particular, its ability to detect and isolate the malicious agent whenever possible.

PUBLISHED PAPERS

• El Qemmah, A., Casavola, A., Tedesco, F. (2025). "Privacy-Preserving Distributed Cooperative Command Governor Schemes." 2025 American Control Conference (ACC), Denver, CO, USA. [Link 1] - [Link 2]
• El Qemmah, A., Casavola, A., Tedesco, F. (2025). "On the Detection of Deception Attacks and Resilience in Turn-Based Command Governors for Dynamically Decoupled Systems." IFAC PapersOnLine, vol. 59, no. 27, pp. 196–201. [Link 1] - [Link 2]
• Tedesco, F., Gagliardi, G., & Casavola, A. (2025). "Command governor schemes for dynamical systems subject to rate-bounded disturbances." Systems & Control Letters, vol. 196, article 106015. [Link 1]
• El Qemmah, A., Casavola, A., Tedesco, F., Sinopoli, B. (2024). "Data-Driven Command Governors for Discrete-Time LTI Systems with Linear Constraints." 63rd IEEE Conference on Decision and Control (CDC), Milan, Italy. [Link 1]
• Tedesco, F., Casavola, A. (2024). "Distributed Supervision Strategies for Cyber-Physical Systems With Varying Network Topology." IEEE Transactions on Automatic Control, vol. 69, no. 8, pp. 5408–5423. [Link 1]
• Casavola, A., D’Angelo, V., El Qemmah, A., Gagliardi, G., Tedesco, F., Torchiaro, F. A. (2024). "A Matlab-Based Toolbox for Supervising Multi-Vehicle Autonomous Systems." IEEE Access, vol. 12, pp. 127051–127064. [Link 1]
• Casavola, A., D’Angelo, V., El Qemmah, A., Gagliardi, G., Tedesco, F., Torchiaro, F. A. (2025). "Dynamic Distributed Coordination Schemes for Multi-Mobile Robot Systems Under Collision Avoidance Constraints." IEEE Transactions on Automation Science and Engineering (TASE), vol. 22, no. 4, pp. 19912 - 19928. [Link 1] - [Link 2]
• Casavola, A., D’Angelo, V., El Qemmah, A., Gagliardi, G., Tedesco, F., Torchiaro, F. A. (2024). "Preserving Agents Connectivity Amidst Static Obstacles: A Distributed Predictive Supervision Approach within Robot Operating System." IEEE Transactions on Intelligent Vehicles, (Early Access). [Link 1]
• S. Manfredi, "Robust Consensus Design of Uncertain Multiagent Systems With Bounded Gains and Incremental Nonlinear Interactions," IEEE Transactions on Industrial Informatics, vol. 20, no. 10, pp. 11844-11853, Oct. 2024. [Link 1] - [Link 2]
• D. Angeli and S. Manfredi, "Consensus Protocols in Networks With 1-to-N Joint-Agent Interactions," IEEE Transactions on Automatic Control, vol. 70, no. 2, pp. 1273-1280, Feb. 2025. [Link 1] - [Link 2]
• S. Manfredi, D. Angeli and C. Tortora, "A Resilient Consensus Algorithm With Inputs for the Distributed Monitoring of Cyber-Physical Systems," Control Engineering Practice, vol. 154, p. 106166, 2025. [Link 1]
• S. Manfredi, "Robust Dynamic Corrective Consensus Over Lossy Networks With Gilbert-Elliott Channels," IEEE Transactions on Automatic Control, vol. 70, no. 6, pp. 4024-4030, June 2025. [Link 1] - [Link 2]
• S. Manfredi and L. Molino, "Resilient Distributed Kalman Filtering for Cyber-Physical Systems via Mean Subsequence Reduction," Information Fusion, vol. 133, p. 104218, 2026. [Link 1]
• L. Molino, A. De Marco and S. Manfredi, "Energy-Based Multiple-Input-Multiple-Output Nonlinear Control of Fixed-Wing Aircraft," in Proceedings of the Fourth International Nonlinear Dynamics Conference (NODYCON 2025), Stevens Institute of Technology, Hoboken, NJ, USA, Jun. 22-25, 2025. [Link 1] - [Link 2]
• D. Angeli, D. Martini, G. Innocenti, S. Manfredi and L. Molino, "A VCG Mechanism for Optimal Social Operation of Shared Batteries in Energy Communities," in Proceedings of the IFAC Joint Conference on Computers, Cognition and Communication, Centro Culturale Altinate, Padova, Italy, Sep. 15-18, 2025. [Link 1] - [Link 2]
• S. Manfredi, L. Molino, D. Angeli, D. Martini and G. Innocenti, "VCG-Based Incentive-Compatible LQ Control for Truthful and Efficient Vehicle Platooning," in Proceedings of the IFAC Joint Conference on Computers, Cognition and Communication, Centro Culturale Altinate, Padova, Italy, Sep. 15-18, 2025. [Link 1] - [Link 2]
• Tianyi Zhong and David Angeli, "Incentive Mechanism Design for Carbon-Aware Electric Vehicle Charging Coordination Problem," in IEEE Control Systems Letters, vol. 10, pp. 79-84, 2026, doi:10.1109/LCSYS.2026.3660095. [Link 1] - [Link 2]
• Tianyi Zhong, David Angeli, “A truthful mechanism design for distributed optimisation algorithms in networks with self-interested agents,” Automatica, Vol. 184, 2026, 112727, ISSN 0005-1098. [Link 1]
• Tianyi Zhong and David Angeli, “A Cutting Plane-Based Distributed Algorithm for Non-Smooth Optimisation With Coupling Constraints,” IEEE Control Systems Letters, Vol.8, pp. 1223-1228, 2024. [Link 1] - [Link 2]
• David Angeli, Sabato Manfredi, Tianyi Zhong, A causal filter of gradient information for enhanced robustness and resilience in distributed convex optimization, Systems & Control Letters, Volume 181, 2023. [Link 1]
• D. Angeli, D. Martini, G. Innocenti and A. Tesi, "An LMI Formulation of Small-Gain Theorems for 2-Contraction of Nonlinear Interconnected Systems," in IEEE Transactions on Automatic Control, vol. 70, no. 9, pp. 6214-6221, Sept. 2025, doi: 10.1109/TAC.2025.3558142. [Link 1] - [Link 2]
• Joshua L. Kurniawan, David Angeli, Guaranteed benefit collusion strategies for Vickrey–Clarke–Groves mechanism, Nonlinear Analysis: Hybrid Systems, Volume 61, 2026, 101716, ISSN 1751-570X [Link]

SUBMITTED PAPERS

• El Qemmah, A., Mohamed M., Casavola, A., Tedesco, F. (2026), “Adversarial Non Compliance in Turn-Based Command Governors: Optimal Attack Synthesis with Feasibility Guarantees”, Submitted 2026. [Link]
• S. Manfredi, D. Angeli and L. Molino, "A Flexible Algorithmic Approach to Distributed Social Optimization of Non-Extreme Agents," manuscript submitted to Automatica, 2025. [Link]
• L. Molino and S. Manfredi, "Balancing Resilience and Optimality in Distributed Optimization for Cyber-Physical Systems," manuscript submitted to Expert Systems with Applications, 2025. [Link]
• Manfredi, S., Molino, L., Angeli, D., Innocenti, G., Martini, D. (2025). “Socially Optimal Linear Quadratic Control with Resilience for Vehicle Platooning.” Conditionally accepted, IEEE Transactions on Automation Science and Engineering. [Link]
• D. Angeli, G. Innocenti, S. Manfredi, D. Martini and L. Molino, "A VCG Mechanism for Socially Optimal Linear Quadratic Regulation in the Presence of Stake-Holders With a Common Set-Point," manuscript submitted to Systems & Control Letters, 2025. [Link]

CASE STUDIES

• Casavola, A., Tedesco F, Torchiarto F.A. (2026) "Frequency/Power Control in Electrical Systems with Renewable Sources Subject to Bounded Disturbances", Technical Report, Unical. [Link]

The 2014-2020 Italian National Research Program (PNR) identifies twelve thematic areas of scientific/technical specialization around which to structure the national and regional policies and instruments to support the research and increase the impact on the social and economic development of the country. The twelve areas are identified starting from the combination between the conceptual categories derived from the “Challenges of Society” and the “Key Enabling Technologies” present in the Horizon 2020 program on the one hand, and, on the other, the national specificities, enhancing the results already achieved from national and regional policies on research and innovation. Moreover, the PNR program identifies five specific industrial domains which constitute the main areas of application of the public/private skills that are generated in the twelve areas of specialization. These are specified in the National Strategy of Intelligent Specialization (SNSI) and are: Aerospace and defense; Health, nutrition, quality of life; Smart and sustainable industry, energy and environment; Tourism, cultural heritage and creativity industry; Digital Agenda, Smart Communities, infrastructures and intelligent mobility systems. In addition, the EU 2050 long-term strategy for a prosperous, modern, competitive and climate-neutral economy by 2050 and the 2022 IPC (International Panel on Climate Change) both recommend a significant reduction of greenhouse gas (GHG) emissions and more efficient use of the resources.

CPSs are transforming the way people interact with engineering systems as they integrate sensing, computation, control and networking into physical objects and infrastructures. Such networks of smart devices represent a new formal paradigm to model modern engineering systems, applications and services ranging from intelligent transport systems, coordination of a fleet of vehicles and drones, to smart grids, etc. The conceptual ideas and control methodologies developed in this project for CPSs appear adequate to address the control challenges arising in many large-scale industrial domains and in particular for those identified as a priority in the H2020 and PNR programs, evidenced by the increasing number of applications in recent years.

The case studies addressed in the project are relevant examples of applications where the distributed EMPC and CG methodologies could have a great beneficial impact. They all are among the Horizon 2020 societal challenges on "Secure, clean and efficient energy", "Climate Action, Environment, Resource Efficiency and Raw Materials", "Reducing energy consumption and carbon footprint by smart and sustainable use" and "A single, smart European electricity grid". Although weather-dependent renewable energy has many advantages over traditional energy generation, the intermittency of these resources, due to their variable nature, represents a real problem for their integration into the grid. This requires a careful design and planning of the operations of such systems and networks. Given the variety of energy sources, it will be required to build up new generation sites, set up new grids and control and monitoring strategies capable of managing bidirectional flows of energy, handling voltage drops and managing higher or lower energy requirements. The reliability of the power grid is guaranteed by a continuous and instantaneous balancing of the supply and demand of energy and its stability by planning the existence of adequate levels of energy reserves. Besides a cleaner, cheaper and smarter energy generation, this third macro-goal requires the adoption of process technologies and operation strategies that significantly reduce their energy demand, through coordinated, reliable, real-time optimization and monitoring strategies. The Economic MPC and CG approaches that will be developed in this project will represent a solid basis for new ICT technologies applicable to a wide range of different contexts, all sharing common features of systems interconnection, complexity and need for Resilience. In today's and tomorrow's society and economy, the current operational paradigm that hierarchically separates economic (steady-state) optimization from dynamic control is no longer able to face such situations.

Other examples of applications can be found in the literature.

1. 1) Research efforts on the efficient use of drinkable water and the economically sustainable management of their production and distribution plants have been recently presented in the literature for the Barcelona distribution plant. These activities have impacts on the H2020 societal challenge "Climate Action, Environment, Resource Efficiency and Raw Materials". Rapid demographic, socio-economic and climate changes are threatening the sustainable development of the Mediterranean region, especially the capacity of its agriculture to cope with increased demand for food production in a scenario of water scarcity and increasing competition for water use between different sectors. To address this challenge, it is recognized that a significant and well-coordinated research effort at the regional scale is needed to find innovative solutions to sustainable food production and water use.
2. 2) The UniCAL RU is involved in the ongoing PON project ARES that aims at developing distributed CG strategies for the coordination of multi-vehicles autonomous surface marine vessels for environmental monitoring or surveillance. Because the vehicles are autonomous and unmanned they are expected to generate their routes by themselves. Therefore, when working in the same restricted area, coordination is necessary to avoid collisions and obstacles avoidance. Moving in formation has many advantages, for example, it can reduce system cost, increase robustness and efficiency by providing additional redundancy and flexibility. A second reason for the use of vehicle formations lies in the possibility of exploiting heterogeneity of the equipment of each individual vehicle, which together can give rise to new and more complete functions. Finally, a third reason is that more mobile devices equipped with a certain sensor can cover a region of interest faster than a single vehicle when the detection range is too large to cover the entire area from a single location or to allow joint measurements that are difficult to obtain from a single-vehicle.
3. 3) A case study of Economic optimization of HVAC systems based on the Stanford University Campus has been recently proposed in the literature. Commercial buildings account for $200 billion per year in energy expenditures, with heating, ventilation, and air conditioning (HVAC) systems accounting for most of these costs. In energy markets with time-varying prices and peak demand charges, a significant potential for cost savings is provided by using thermal energy storage to shift energy loads. Since most implementations of HVAC control systems do not optimize energy costs, they have become a primary focus for new strategies aimed at economic optimization. However, some industrial applications, such as large research centers or university campuses, are too large to be solved in a single MPC instance. Decompositions have been proposed in the literature, but it is difficult to evaluate and compare decompositions against one another when using different systems. The case study shows that solving a single MPC optimization problem is not feasible for real-time implementations whereas a distributed scheme succeeds. The study is loosely based on the Stanford University campus, consisting of both an airside and waterside system. The airside system includes 500 zones spread throughout 25 campus buildings along with the air handler units and regulatory building automation system used for temperature regulation. The waterside system includes the central plant equipment, such as chillers, that is used to meet the load from the buildings.
4. Intelligent mobility is another big domain where the control strategies proposed in this project might have a large impact. Next-generation of connected cars will communicate with each other and with the road infrastructures. Smart traffic management will benefit from schemes of coordination where traffic lights and signs interact with each other and with the cars, thereby adjusting to the evolving traffic scenarios to minimize the time vehicles waste waiting. Moreover, fleets of self-driving vehicles and personal services such as car-sharing and ride-sharing will be available in the next future. It is increasingly recognized that to get full advantage from autonomous vehicles traffic and pollution reduction, and to support the effective implementation of the abovementioned mobility solutions, a number of situations will require coordinating the relative activities and movements of vehicles. This trend is witnessed by initiatives such as the Grand Cooperative Driving Challenge (https://doi.org/10.1109/MWC.2016.7553038), which in its latest edition explicitly focused on cooperative automated driving. Examples of very diverse situations that require proper and careful coordination amongst groups of vehicles include: crossing intersections, entering a motorway, platooning, organizing urban deployment and rides for fleets of ride/car-sharing vehicles, trying to improve parking occupancies and reducing parking times.

Adversarial or malicious behaviors in all the above contexts can be easily imagined arising from cyber-attacks, non-cooperative behaviors of agents and cyber/physical faults. Thus, the automatic detection and isolation of these anomalies in real-time and the study of robust distributed coordination of these large-scale systems in the presence of malicious behaviors are expected to have a large impact on their development and use.