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 (EMPC) [1] and Command Governors (CGs) [2], 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 [3] underlying the online command computation with respect to the 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 [20].
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.
It is widely recognized that, for solving optimization problems over cyber-physical networks, it is not possible to apply the classical centralized optimization algorithms as it is not efficient or possible to collect and manage data from the overall network at a unique node. Moreover, information privacy is often a requirement as part of data at each node must not be shared with the other nodes. These challenges have been a driving force to a novel branch of research termed distributed optimization.
Earlier ideas and solutions surveyed in Sect.1 of this proposal, which represent the current state of the art in the field, don’t consider important aspects that cannot any longer be neglected in the analysis and synthesis of modern CPSs and will be the object of this proposal. They are the
- Resilience to cyber-attacks and adversarial behaviors. The first aim is to make CPSs resilient to cyber-attacks, malicious node behaviors and unreliable communications by developing distributed control and supervision schemes and underlying optimization algorithms that are robust to adversarial behaviors and agents.
- Reliability. In addition to cyber-attacks, there exist other uncertainties in CPSs, such as packet drops, faults and link failures that require forms of control reconfiguration, e.g. Plug-&-Play functionalities, to be effective.
- Privacy: Data exchange among nodes is widely used in distributed cooperative optimization, where all the agents in the network constantly communicate and update their states in order to achieve an agreement on the global minimizer. This may involve an undesirable disclosure of information of an agent (i.e. costs, local gradient, values of critical variables) to the other agents that may not be acceptable in some applications.
- Greenness: The rapid incorporation of CPSs into future smart-city applications have an impact on the environment. ICT infrastructure and smart devices are critical energy-constrained consumers, which will keep increasing as cyber-physical-system-based technologies become more ubiquitous. Therefore, one of the key factors in reducing the negative impact of ICT on the environment is to apply distributed algorithms to reduce energy consumption and optimize energy management in CPSs.
The program of this project is organized in the following five Work packages: (WP1-WP3) collect the methodological activities, WP4 is dedicated to case studies and WP5 to Dissemination and Coordination.
