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) 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) 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) 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.
- 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.
