MODELING, ANALYSIS, AND CONTROL OF COMPLEX NETWORKS AND CYBER-PHYSICAL SYSTEMS
Modeling, Analysis, and Control of
Complex Networks and Cyber-Physical Systems
Ischia, Biblioteca Comunale Antoniana
Saturday, June 29
Sunday, June 30
Abstracts of the contributions
Distributed Monitoring and Fault-Tolerant Control: Scalable Plug & Play Tools and Industry 4.0 Perspective
This lecture deals with a class of systems that are becoming ubiquitous in the current and future "distributed world" made by countless "nodes", which can be cities, computers, people, etc., and interconnected by a dense web of transportation, communication, or social ties. In an increasingly "smarter" planet, it is expected that such interconnected systems will be safe, reliable, available 24/7, and of low-cost maintenance – the Industry 4.0 vision. Therefore, faults and malfunctions need to be detected promptly and their source and severity should be diagnosed so that corrective actions can be taken as soon as possible. Once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected large-scale system. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in.
In the talk, an adaptive approximation-based distributed fault diagnosis approach for large-scale nonlinear systems will be dealt with, by exploiting a "divide et impera" approach in which the overall diagnosis problem is decomposed into smaller sub-problems, which can be solved within “local” computation architectures. The distributed detection, isolation and identification task is broken down and assigned to a network of "Local Diagnostic Units", each having a "local view" of the system. The following step is the integration of a distributed model predictive control scheme and a distributed fault diagnosis architecture. Being distributed, the whole model of the large-scale system is never used in any step of the design process. We refer to this kind of decentralized synthesis as plug & play design, if - in addition - the plug-in and unplugging operations can be performed through a procedure for automatically assessing whether the operation does not spoil stability and constraint satisfaction for the overall large-scale system.
In the lecture, the connection is finally worked out with Virtual Commissioning which is the very recent trend in the process industry to make the dream of plug & work installation of a reliable and efficient automation system become a reality.
Smart Buildings: Monitoring, Control and Fault Tolerance
Modern buildings are complex systems of structures and technology aimed at providing a safe and comfortable environment for the occupants. Recent advances in information and communication technologies have generated significant interest in developing smart buildings, which provide much greater capabilities in terms of energy efficiency, safety, security, interactivity, as well as in terms of mitigating environmental impact. New components for smart buildings, such as sensors, actuators, controllers, embedded systems and wireless communications, are becoming readily available. Moreover, the Internet-of-Things (IoT) technology is already having a significant impact on developments related to smart buildings. The objective of this presentation is to provide an overview of current advances in smart buildings and to present some results on monitoring, control and fault tolerance of Heating, Ventilation and Air-Conditioning (HVAC) systems, which are crucial components of smart buildings. Various estimation, learning and feedback control algorithms will be presented and illustrated, and directions for future research will be discussed.
The role of non-normal dynamics for the efficient information transmission in networks
While there exists a consistent number of contributions in computational neuroscience that support the hypothesis that non-normality makes the information transmission more efficient in neuronal networks, no quantitative analysis has been proposed, analysis that would allow the comparison of different network topologies. In this contribution we propose a model that puts in a precise frame the arguments proposed in those papers. Based on this model we propose a metric that, through the Shannon capacity of a suitable channel build on the network, enables to quantify the information transmission efficiency. This allows to confirm that non-normality enhance the channel capacity, but only in the high noise regime. Finally we specify which non-normality degree plays a role in this enhancement.
Structure preserving reduction of networks: classical control methods
Most of the current reduction techniques for networks of systems rely on clustering, because they easily preserve the network structure in the reduced model. Application of the classical methods that are very relevant from a control systems perspective, such as balancing and moment matching based model reduction, generally do not preserve the network structure. In this presentation we focus on the generalisation of these classical methods so that both the amount of nodes and the (passive) dynamics of nodes are reduced, while preserving relevant network and (passive) dynamics structures. The developments are done for networks of linear systems, as well as networks with Lur’e dynamics on the nodes. Furthermore, a priori error bounds will be provided, optimal weight allocation for the reduced order network is considered and relevant small and large scale examples will be used to illustrate the results.
Dynamics and regularities in the structure: opportunities for control
In this talk I will start discussing the notion of symmetries and equitable partitions for complex networks and then illustrate how they impact the dynamics on the networks, focusing in particular on consensus and synchronization. In the second part of the talk I will show how these notions could be exploited to design control strategies for inducing synchronization in a subset of the nodes of the network or multi-consensus regimes.
Emerging control problems in complex networks
Complex networks theory was born from the need of explaining how ensembles of interacting units may give rise to fascinating emerging behaviors that cannot be explained by only looking at the individual dynamics. Accordingly, it could be considered the natural framework for studying a wide range of phenomena in very diverse disciplines. However, classic complex networks theory is based on a set of simplifying assumptions to guarantee analytical tractability, which immediately appear inconsistent when we aim at controlling real-world complex systems.
Optimal control of networks: energy scaling and open challenges
Recent years have witnessed increased interest from the scientific community regarding the control of complex dynamical networks. Some common types of networks examined throughout the literature are power grids, communication networks, gene regulatory networks, neuronal systems, food webs, and social systems. Optimal control studies strategies to control a system that minimize a cost function, for example the energy that is required by the control action.
We show that by controlling the states of a subset of the nodes of a network, rather than the state of every node, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs, as long as the target set is appropriately sized.
Control Theory for Practical Cyber-Physical Security
In this talk, we discuss how control theory can contribute to the analysis and design of secure cyber-physical systems. We start by reviewing conditions for detectability and impact of data attacks targeting feedback control loops running over a field communication network. We investigate three different attack scenarios: Sensor attacks, actuator attacks, and coordinated actuator and sensor attacks. In particular, we highlight how a physical understanding of the controlled process can guide us in the allocation of counter measures and limit the possible impact of attacks.
Modeling, Analysis and Design of Resilient Cyber-Physical Systems
Recent advances in sensing, communication and computing allow cost effective deployment in the physical world of large-scale networks of sensors and actuators, enabling fine grain monitoring and control of a multitude of physical systems and infrastructures. Such systems, called cyber-physical, lie at the intersection of control, communication and computing. The close interplay among these fields renders independent design of the control, communication, and computing subsystems a risky approach, as separation of concerns does not constitute a realistic assumption in real world scenarios. It is therefore imperative to derive new models and methodologies to allow analysis and design of robust and secure cyber-physical systems (CPS). In this talk I will present an overview of recent research on the topic and discuss future directions.
Distributionally Robust Learning with Applications to Health Analytics
We will present a distributionally robust optimization approach to learning predictive models, using general loss functions that can be used either in the context of classification or regression. Motivated by medical applications, we assume that training data are contaminated with (unknown) outliers. The learning problem is formulated as the problem of minimizing the worst case expected loss over a family of distributions within a certain Wasserstein ball centered at the empirical distribution obtained from the training data. We will explore the generality of this approach, its robustness properties, its ability to explain a host of "ad-hoc" regularized learning methods, and we will establish rigorous out-of-sample performance guarantees.
Beyond predictions, we will discuss methods that can leverage the robust predictive models to make decisions and offer specific personalized prescriptions and recommendations to improve future outcomes. We will provide some examples of medical applications of our methods, including predicting hospitalizations for chronic disease patients, predicting hospital length-of-stay for surgical patients, and making treatment recommendations for diabetes and hypertension.
E-bikes as a paradigm to design human-in-the-loop cyber-physical systems
In this talk we present the design of a cyber-physical control system for an intelligent e-bike. The system, which is deployed on a real-world testbed, leverages tools from data analytics, stochastic processes and control to manage the interactions between the cyclist and the bike motor. Our ultimate goal is to influence the cycling behavior and an application concerned with the regulation of the cyclist breathing rate to minimize his/her intake of environmental pollution is discussed. After presenting experimental and theoretical results, we outline and generalize some of the main challenges of our design, which are underpinned by dynamical systems and control theory.
Distributed Resilient Control of Dynamic Flows in Transportation Networks
Ever-growing loads, limited infrastructure capacity, and new technologies enabling the use of intelligence at unprecedented levels have created significant new challenges in the control of transportation networks. Due to their level of interconnectedness and the complex interactions between cyber and physical layers and human decision makers, these systems may exhibit inefficiencies and fragilities. This talk will present recent results on resilience and efficiency of distributed control architectures for dynamic flow networks with with applications to traffic signal control, dynamic pricing, and route guidance systems.
Multi-agent Map-building: Kalman Filtering for Gaussian Processes
The proliferation of large scale smart multi-agent systems, also known as Internet-of-Things, Networked Control Systems, Wireless sensor and actuator networks, Cyber-physical Systems, etc., are providing us with a wealth of data with unprecedented time-space resolution which can trigger the next technological revolution. However, this trend is also posing a formidable challenge, often referred as Data Tsunami, which requires the analysis of a large-scale correlated time-series. In this talk, the problem of estimating a map will be addressed, first in a static scenario and later in a dynamic scenario, based on noisy measurements collected by a large number of sensors in the presence of unreliable communication. In particular, pros and cons of parametric and non-parametric estimation approaches will be discussed and some strategies are proposed to merge ideas from control theory such as Gauss-Markov estimators and Kalman Filtering, and from Machine Learning such as Gaussian regression, Karhunen-Loève kernel expansions and Nystrom method.
Selected talks by participants
Emerging Complexity in Business IT Alignment Dynamics
Despite three decades of theoretical and empirical research, aligning IT and business in companies is still considered an unachieved objective in corporate practice. In our study we address alignment as a process within companies where different socio-technical components interact, exhibiting complex dynamics. In this talk we present and discuss a mathematical model embedding the key parameters that influence the alignment process: management and technical skills of the IT department, flexibility of the Information System and of the personnel, pressure exerted by the management, and IT investment policy of the organisation. Simulation of the model shows that alignment is a complex process, where the number and stability of the equilibrium states change according to the values of the model’s parameters, and complex oscillatory regimes are possible. The alignment dynamics exhibited by the model’s simulation has been compared to data collected in a set of case studies to prove, qualitatively, the model’s validity. Despite its simplicity, the model well captures the dynamics of alignment in a real environment and provides suggestions on how to improve and manage the alignment implementation in companies.
Synchronization of Piecewise-Smooth Networks
Complex networks have been attracting the attention of researchers from diverse fields for many years, as they are often found in applications. However, in practice it is not uncommon to find dynamical systems that are described by piecewise-smooth models, such as electronic switching circuits, mechanisms affected by dry friction, neurons and cardiac cells, and so on. The study of emergent behaviour, and in particular synchronization, has applicability in seismology, for what concerns the dynamics of neighbouring faults, in determining convergence of frequency in power grids, and more. To enforce global asymptotic state synchronization, we propose the addition of a discontinuous coupling action to the commonly used diffusive coupling, without the need of any costly and impractical centralised control strategy. In addition, we show that the critical value on the coupling gain associated to the discontinuous coupling protocol depends on the newly defined minimum density, i.e. the density of the sparsest cut in the graph. This crucial quantity, acts similarly to the algebraic connectivity in the case of networks of smooth systems, in illustrating the relation between synchronizability and topology.
Applying MPC to Container Transport Planning
To improve the efficiency of the container transport system various organizations try to stimulate more flexible transport schemes, where the service of transport is bought rather than a series of connections. The improved efficiency is expected to manifest itself as improved utilization rates and increased usage of the cheaper and more sustainable barge and train modes. This will mainly be achieved by utilizing the flexible transport schemes’ capability of adjusting the transport plans in real-time. In our research we address the needed real-time freight transport planning with model predictive control (MPC).
Complex networks controllability
Controllability of complex networks is a topic that has been thoroughly studied of late. As complex networks are not designed to be controlled, the viewpoint has risen that this structural property is to be endowed to a network rather than verified. To this aim, various driver node selection problems on linear dynamical networks have been tackled, where one must decide in which nodes the control signals must be injected so to endow a network of some well-defined controllability properties. This talk focuses on the case in which physical or economic constraints ensure the network cannot be made completely controllable and/or observable, and provides the tools to cope with what turns out being a very delicate scenario. Taken altogether, the presented results provide the basis for a novel network decomposition, one that is inspired, but that is not equal, to Kalman’s decomposition for linear dynamical systems.
Formation control of stochastic non-holonomic vehicles
Among popular mathematical frameworks to study mobile robots, second order non-holonomic unicycle models provide a more realistic framework to study the 2-D motion of ground robots with one castor, two differentially driven wheels, and fixed wind aircrafts. In the literature of multi-agent systems, despite considerable efforts in the study of the impact of additive noise on group behavior, the effect of perturbations, due to internal and external factors, on control inputs, such as the actuation force and torque, has received less attention. In this work, we devise a novel pinning cooperative control strategy that is applied in a stochastic setting to a subset of agents to drive the entire group towards a desired formation pattern. The system is modelled as a network of interacting agents including single or few informed individuals known as group leaders, while the rest of the group updates their state by interacting with connected neighbors. The effectiveness of the control scheme in the presence of stochastic noise is demonstrated using Lyapunov stability theory. Numerical simulations are performed to investigate the robustness of the system to increased noise on both the input force and torque. Finally, the proposed control strategy is experimentally validated on ground robots.
Decentralized throughput-optimal traffic signal control
With the growing demand in transportation networks, there is a need for better resource utilization. The recent development in sensing technology has made it possible to incorporate more feedback control into the network to achieve this goal. In this talk, I will present a decentralized control strategy for traffic signal control, referred to as the Generalized Proportional Allocation (GPA) controller. The only information the controller needs is the queue lengths in the signalized junction it is serving. This low requirement about exogenous information makes the controller robust to perturbations in the system. Still, the GPA controller is throughput optimal, which means that if any controller can stabilize the network, the GPA can stabilize it too. While the controller is analyzed for a simplified dynamical model, validation of it has also been performed in a microscopic traffic simulator, SUMO, in a scenario covering the city of Luxembourg. The simulations show that the GPA performs better than another well-known decentralized traffic signal control strategy, the MaxPressure controller, during low and medium demands scenarios and sometimes also for high demands.
Control and stability analysis of DC microgrids through contraction theory
DC Microgrids have emerged as an appropriate solution to the world energy crisis by integrating renewable energy sources, storage devices and modern loads. One of the main aspects studied in DC microgrids is the development of control strategies in a distributed manner that guarantee stability under large disturbances provided by variations in the loads. Despite satisfactory performance offered by the existing control strategies, these neglect the switching bechavior introduced by power converters showed in real implementations, usually assuming an averaged model or via linearizations.To address this challenge, in this talk I will present sufficient conditions that assure exponential stability of a DC microgrid formed by an interconnection DC-DC buck power converters operated by switching control strategies. A numerical validation verify the efectiveness of the proposed criterion.