Publications
51 results found
Casagrande V, Prodan I, Spurgeon SK, et al., 2022, Resilient Microgrid Energy Management Algorithm Based on Distributed Optimization, IEEE SYSTEMS JOURNAL, ISSN: 1932-8184
Alessandri A, Boem F, 2020, State Observers for Systems Subject to Bounded Disturbances Using Quadratic Boundedness, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 65, Pages: 5352-5359, ISSN: 0018-9286
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- Citations: 4
Gallo A, Turan M, Boem F, et al., 2020, A distributed cyber-attack detection scheme with application to DC microgrids, IEEE Transactions on Automatic Control, Vol: 65, Pages: 3800-3815, ISSN: 0018-9286
DC microgrids often present a hierarchical control architecture, requiring integration of communication layers. This leads to the possibility of malicious attackers disrupting the overall system. Motivated by this application, in this paper we present a distributed monitoring scheme to provide attack-detection capabilities for linear Large-Scale Systems. The proposed architecture relies on a Luenberger observer together with a bank of Unknown-Intput Observers (UIOs) at each subsystem, providing attack detection capabilities. We describe the architecture and analyze conditions under which attacks are guaranteed to be detected, and, conversely, when they are stealthy . Our analysis shows that some classes of attacks cannot be detected using either module independently; rather, by exploiting both modules simultaneously, we are able to improve the detection properties of the diagnostic tool as a whole. Theoretical results are backed up by simulations, where our method is applied to a realistic model of a low-voltage DC microgrid under attack.
Barboni A, Rezaee H, Boem F, et al., 2020, Detection of covert cyber-attacks in interconnected systems: a distributed model-based approach, IEEE Transactions on Automatic Control, Vol: 65, Pages: 3728-3741, ISSN: 0018-9286
Distributed detection of covert attacks for linear large-scale interconnected systems is addressed in this article. Existing results consider the problem in centralized settings. This article focuses on large-scale systems subject to bounded process and measurement disturbances, where a single subsystem is under a covert attack. A detection methodology is proposed, where each subsystem can detect the presence of covert attacks in neighboring subsystems in a distributed manner. The detection strategy is based on the design of two model-based observers for each subsystem using only local information. An extensive detectability analysis is provided and simulation results on a power network benchmark are given, showing the effectiveness of the proposed methodology for the detection of covert cyber-attacks.
Li P, Boem F, Pin G, et al., 2020, Kernel-based simultaneous parameter-state estimation for continuous-time systems, IEEE Transactions on Automatic Control, Vol: 65, Pages: 3053-3059, ISSN: 0018-9286
In this note, the problem of jointly estimating thestate and the parameters of continuous-time systems is addressed.Making use of suitably designed Volterra integral operators,the proposed estimator does not need the availability of time-derivatives of the measurable signals and the dependence ontheunknown initial conditions is removed. As a result, the estimatesconverge to the true values in arbitrarily short time in noise-freescenario. In the presence of bounded measurement and processdisturbances, the estimation error is shown to be bounded. Thenumerical implementation aspects are dealt with and extensivesimulation results are provides showing the effectivenessof theestimator.
Boem F, Gallo A, Raimondo DM, et al., 2020, Distributed fault-tolerant control of large-scale systems: An active fault diagnosis approach, IEEE Transactions on Control of Network Systems, Vol: 7, Pages: 288-301, ISSN: 2325-5870
The paper proposes a methodology to effectively address the increasingly important problem of distributed faulttolerant control for large-scale interconnected systems. The approach dealt with combines, in a holistic way, a distributed fault detection and isolation algorithm with a specific tube-based model predictive control scheme. A distributed fault-tolerant control strategy is illustrated to guarantee overall stability and constraint satisfaction even after the occurrence of a fault. In particular, each subsystem is controlled and monitored by a local unit. The fault diagnosis component consists of a passive set-based fault detection algorithm and an active fault isolation one, yielding fault-isolability subject to local input and state constraints. The distributed active fault isolation module - thanks to a modification of the local inputs - allows to isolate the fault that has occurred avoiding the usual drawback of controllers that possibly hide the effect of the faults. The Active Fault Isolation method is used as a decision support tool for the fault tolerant control strategy after fault detection. The distributed design of the tube-based model predictive control allows the possible disconnection of faulty subsystems or the reconfiguration of local controllers after fault isolation. Simulation results on a well-known power network benchmark show the effectiveness of the proposed methodology.
Boem F, Carli R, Farina M, et al., 2019, Distributed fault detection for interconnected large-scale systems: a scalable plug & play approach, IEEE Transactions on Control of Network Systems, Vol: 6, Pages: 800-811, ISSN: 2325-5870
In this paper, we propose a novel distributed fault detection method to monitor the state of a - possibly large-scale - linear system, partitioned into interconnected subsystems. The approach hinges on the definition of a partition-based distributed Luenberger-like estimator, based on the local model of the subsystems and that takes into account the dynamic coupling between the subsystems. The proposed methodology computes - in a distributed way - a bound on the variance of a properly defined residual signal. This bound depends on the uncertainty affecting the state estimates computed by the neighboring subsystems and it allows the computation of local fault detection thresholds, as well as the maximum false-alarms rate. The implementation of the proposed estimation and fault detection method is scalable, allowing Plug & Play operations and the possibility to disconnect the faulty subsystem after fault detection. Theoretical conditions on the convergence properties of the estimates and of the estimation error bounds are provided. Simulation results on a power network benchmark show the effectiveness of the proposed method.
Barrere Cambrun M, Hankin C, Barboni A, et al., 2019, CPS-MT: a real-time cyber-physical system monitoring tool for security Research, 24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA2018), Publisher: IEEE
Monitoring systems are essential to understand and control the behaviour of systems and networks. Cyber-physical systems (CPS) are particularly delicate under that perspective since they involve real-time constraints and physical phenomena that are not usually considered in common IT solutions. Therefore, there is a need for publicly available monitoring tools able to contemplate these aspects. In this poster/demo, we present our initiative, called CPS-MT, towards a versatile, real-time CPS monitoring tool, with a particular focus on security research. We first present its architecture and main components, followed by a MiniCPS-based case study. We also describe a performance analysis and preliminary results. During the demo, we will discuss CPS-MT’s capabilities and limitations for security applications.
Boem F, Sabattini L, Secchi C, 2019, Decentralized state estimation for the control of network systems, Journal of The Franklin Institute, Vol: 356, Pages: 860-882, ISSN: 0016-0032
The paper proposes a decentralized state estimation method for the control of network systems, where a cooperative objective has to be achieved. The nodes of the network are partitioned into independent nodes, providing the control inputs, and dependent nodes, controlled by local interaction laws. The proposed state estimation algorithm allows the independent nodes to estimate the state of the dependent nodes in a completely decentralized way. To do that, it is necessary for each independent node of the network to estimate the control input components computed by the other independent nodes, without requiring communication among the independent nodes. The decentralized state estimator, including an input estimator, is developed and the convergence properties are studied. Simulation results show the effectiveness of the proposed approach.
Boem F, Riverso S, Ferrari-Trecate G, et al., 2019, Plug-and-play fault detection and isolation for large-scale nonlinear systems with stochastic uncertainties, IEEE Transactions on Automatic Control, Vol: 64, Pages: 4-19, ISSN: 0018-9286
This paper proposes a novel scalable model-based fault detection and isolation approach for the monitoring of nonlinear large-scale systems, consisting of a network of interconnected subsystems. The fault diagnosis architecture is designed to automatically manage the possible plug-in of novel subsystems and unplugging of existing ones. The reconfiguration procedure involves only local operations and communication with neighboring subsystems, thus, yielding a distributed and scalable architecture. In particular, the proposed fault diagnosis methodology allows the unplugging of faulty subsystems in order to possibly avoid the propagation of faults in the interconnected large-scale system. Measurement and process uncertainties are characterized in a probabilistic way leading to the computation, at each time-step, of stochastic time-varying detection thresholds with guaranteed false-alarms probability levels. To achieve this goal, we develop a distributed state estimation scheme, using a consensus-like approach for the estimation of variables shared among more than one subsystem; the time-varying consensus weights are designed to allow plug-in and unplugging operations and to minimize the variance of the uncertainty of the fault diagnosis thresholds. Convergence results of the distributed estimation scheme are provided. A novel fault isolation method is then proposed, based on a generalized observer scheme and providing guaranteed error probabilities of the fault exclusion task. Detectability and isolability conditions are provided. Simulation results on a power network model comprising 15 generation areas show the effectiveness of the proposed methodology.
Gallo AJ, Turan MS, Boem F, et al., 2018, Distributed watermarking for secure control of microgrids under replay attacks, 7th IFAC Workshop on Distributed Estimation and Control in Networked Systems NECSYS 2018, Pages: 182-187
© 2018 The problem of replay attacks in the communication network between Distributed Generation Units (DGUs) of a DC microgrid is examined. The DGUs are regulated through a hierarchical control architecture, and are networked to achieve secondary control objectives. Following analysis of the detectability of replay attacks by a distributed monitoring scheme previously proposed, the need for a watermarking signal is identified. Hence, conditions are given on the watermark in order to guarantee detection of replay attacks, and such a signal is designed. Simulations are then presented to demonstrate the effectiveness of the technique.
Gallo AJ, Turan MS, Nahata P, et al., 2018, Distributed Cyber-Attack Detection in the Secondary Control of DC Microgrids, Pages: 344-349
The paper considers the problem of detecting cyber-attacks occurring in communication networks typically used in the secondary control layer of DC microgrids. The proposed distributed methodology allows for scalable monitoring of a microgrid and is able to detect the presence of data injection attacks in the communications among Distributed Generation Units (DGUs)- governed by consensus-based control- and isolate the communication link over which the attack is injected. Each local attack detector requires limited knowledge regarding the dynamics of its neighbors. Detectability properties of the method are analyzed, as well as a class of undetectable attacks. Some results from numerical simulation are presented to demonstrate the effectiveness of the proposed approach.
Barboni A, Boem F, Parisini T, 2018, Model-based detection of cyber-attacks in networked MPC-based control systems, 10th International-Federation-of-Automatic-Control (IFAC) Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Publisher: Elsevier, Pages: 963-968, ISSN: 2405-8963
In this preliminary work, we consider the problem of detecting cyber-attacks in a linear system equipped with a Model Predictive Controller, where the feedback loop is closed over a non-ideal network, and the process is subject to a random Gaussian disturbance. We adopt a model-based approach in order to detect anomalies, formalizing the problem as a binary hypothesis test. The proposed approach exploits the analytical redundancy obtained by computing partially overlapping nominal system trajectories over a temporal sliding window, and propagating the disturbance distributions along them. The recorded data over such window is then used to define a probabilistic consistency index at each time step in order to make a decision about the presence of possible attacks. Preliminary simulation results show the effectiveness of the proposed attack-detection method.
Attuati S, Farina M, Boem F, et al., 2018, Reducing false alarm rates in observer-based distributed fault detection schemes by analyzing moving averages, Publisher: ELSEVIER SCIENCE BV, Pages: 473-479, ISSN: 2405-8963
Anagnostou G, Boem F, Kuenzel S, et al., 2018, Observer-based anomaly detection of synchronous generators for power systems monitoring, IEEE Transactions on Power Systems, Vol: 33, Pages: 4228-4237, ISSN: 0885-8950
This paper proposes a rigorous anomaly detectionscheme, developed to spot power system operational changeswhich are inconsistent with the models used by operators. Thisnovel technique relies on a state observer, with guaranteedestimation error convergence, suitable to be implemented in realtime, and it has been developed to fully address this importantissue in power systems. The proposed method is fitted to thehighly nonlinear characteristics of the network, with the statesof the nonlinear generator model being estimated by meansof a linear time-varying estimation scheme. Given the relianceof the existing dynamic security assessment tools in industryon nominal power system models, the suggested methodologyaddresses cases when there is deviation from assumed systemdynamics, enhancing operators’ awareness of system operation.It is based on a decision scheme relying on analytical computationof thresholds, not involving empirical criteria which are likely tointroduce inaccurate outcomes. Since false-alarms are guaranteedto be absent, the proposed technique turns out to be very usefulfor system monitoring and control. The effectiveness of theanomaly detection algorithm is shown through detailed realisticcase studies in two power system models.
Boem F, Zhou Y, Fischione C, et al., 2018, Distributed Pareto-optimal state estimation using sensor networks, Automatica, Vol: 93, Pages: 211-223, ISSN: 0005-1098
A novel model-based dynamic distributed state estimator is proposed using sensor networks. The estimator consists of afiltering step – which uses a weighted combination of sensors information – and a model-based predictor of the system’sstate. The filtering weights and the model-based prediction parameters jointly minimize both the bias and the variance of theprediction error in a Pareto framework at each time-step. The simultaneous distributed design of the filtering weights and ofthe model-based prediction parameters is considered, differently from what is normally done in the literature. It is assumedthat the weights of the filtering step are in general unequal for the different state components, unlike existing consensus-based approaches. The state, the measurements, and the noise components are allowed to be individually correlated, but noprobability distribution knowledge is assumed for the noise variables. Each sensor can measure only a subset of the statevariables. The convergence properties of the mean and of the variance of the prediction error are demonstrated, and they holdboth for the global and the local estimation errors at any network node. Simulation results illustrate the performance of theproposed method, obtaining better results than the state of the art distributed estimation approaches.
Boem F, Gallo AJ, Ferrari-Trecate G, et al., 2018, A distributed attack detection method for multi-agent systems governed by consensus-based control, 56th IEEE Conference on Decision and Control, Publisher: IEEE
The paper considers the problem of detecting cyber-attacks occurring in communication networks for distributed control schemes. A distributed methodology is proposed to detect the presence of malicious attacks aimed at compromising the stability of large-scale interconnected systems and multi-agent systems governed by consensus-based controllers. Only knowledge of the local model is required. The detectability properties of the proposed method are analyzed. A class of undetectable attacks is identified. Preliminary simulation results show the effectiveness of the proposed approach.
Gei C, Boem F, Parisini T, 2018, Optimal System Decomposition for Distributed Fault Detection: Insights and Numerical Results, 10th International-Federation-of-Automatic-Control (IFAC) Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Publisher: ELSEVIER, Pages: 578-585, ISSN: 2405-8963
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Li P, Boem F, Pin G, et al., 2018, Deadbeat Simultaneous Parameter-State Estimation for Linear Continuous-time Systems: a Kernel-based Approach, 17th Annual European Control Conference (ECC), Publisher: IEEE, Pages: 2493-2498
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Kyriacou A, Timotheou S, Reppa V, et al., 2018, Optimization Based Partitioning Selection for Improved Contaminant Detection Performance, 57th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 5568-5573, ISSN: 0743-1546
Li P, Boem F, Pin G, et al., 2018, Fast-convergent Fault Detection and Isolation in an Uncertain Scenario, 57th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 5544-5549, ISSN: 0743-1546
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- Citations: 2
Alessandri A, Boem F, Parisini T, 2018, Model-Based Fault Detection and Estimation for Linear Time Invariant and Piecewise Affine Systems by Using Quadratic Boundedness, 57th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 5562-5567, ISSN: 0743-1546
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- Citations: 4
Gallo AJ, Turan MS, Nahata P, et al., 2018, Distributed Cyber-Attack Detection in the Secondary Control of DC Microgrids, European Control Conference (ECC), Publisher: IEEE, Pages: 351-356
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- Citations: 3
Boem F, Keliris C, Parisini T, et al., 2018, Fault diagnosis for uncertain networked systems, Systems and Control: Foundations and Applications, Pages: 533-581
Fault diagnosis has been at the forefront of technological developments for several decades. Recent advances in many engineering fields have led to the networked interconnection of various systems. The increased complexity of modern systems leads to a larger number of sources of uncertainty which must be taken into consideration and addressed properly in the design of monitoring and fault diagnosis architectures. This chapter reviews a model-based distributed fault diagnosis approach for uncertain nonlinear large-scale networked systems to specifically address: (a) the presence of measurement noise by devising a filtering scheme for dampening the effect of noise; (b) the modeling of uncertainty by developing an adaptive learning scheme; (c) the uncertainty issues emerging when considering networked systems such as the presence of delays and packet dropouts in the communication networks. The proposed architecture considers in an integrated way the various components of complex distributed systems such as the physical environment, the sensor level, the fault diagnosers, and the communication networks. Finally, some actions taken after the detection of a fault, such as the identification of the fault location and its magnitude or the learning of the fault function, are illustrated.
Zhou Y, Boem F, Parisini T, 2017, Partition-based Pareto-optimal state prediction method for interconnected systems using sensor networks, 2017 American Control Conference, Publisher: IEEE, Pages: 1886-1891
In this paper a novel partition-based state prediction method is proposed for interconnected stochastic systems using sensor networks. Each sensor locally computes a prediction of the state of the monitored subsystem based on the knowledge of the local model and the communication with neighboring nodes of the sensor network. The prediction is performed in a distributed way, not requiring a centralized coordination or the knowledge of the global model. Weights and parameters of the state prediction are locally optimized in order to minimise at each time-step bias and variance of the prediction error by means of a multi-objective Pareto optimization framework. Individual correlations between the state, the measurements, and the noise components are considered, thus assuming to have in general unequal weights and parameters for each different state component. No probability distribution knowledge is required for the noise variables. Simulation results show the effectiveness of the proposed method.
Boem F, Reci R, Cenedese A, et al., 2017, Distributed clustering-based sensor fault diagnosis for HVAC systems, 20th IFAC World Congress, Publisher: IFAC / Elsevier, Pages: 4197-4202
The paper presents a distributed Sensor Fault Diagnosis architecture for Industrial Wireless Sensor Networks monitoring HVAC systems, by exploiting a recently proposed distributed clustering method. The approach allows the detection and isolation of multiple sensor faults and considers the possible presence of modeling uncertainties and disturbances. Detectability and isolability conditions are provided. Simulation results show the effectiveness of the proposed method for an HVAC system.
Li PENG, Boem F, Pin G, et al., 2017, Distributed fault detection and isolation for interconnected systems: a non-asymptotic kernel-based approach, 20th IFAC World Congress, Publisher: IFAC
In this paper, a novel framework is proposed for deadbeat distributed Fault Detectionand Isolation (FDI) of large-scale continuous-time LTI dynamic systems. The monitoredsystem is composed of several subsystems which are linearly interconnected with unknownparameterization. Each subsystem is monitored by a local diagnoser based on the measuredlocal output, local inputs and the interconnection variables from the neighboring subsystems.The local FDI decision is based on two non-asymptotic state-parameter estimators using Volterraintegral operators which eliminate the effect of the unknown initial conditions so that theestimates converge to the true value in a deadbeat manner and therefore the fault diagnosiscan be achieved in finite time. Moreover, the unknown interconnection parameters and theunknown fault parameters are simultaneously estimated. Numerical examples are included toshow the effectiveness of the proposed FDI architecture.
Zhou Y, Boem F, Fischione C, et al., 2017, Distributed Fault Detection with Sensor Networks using Pareto-Optimal Dynamic Estimation Method, 2016 European Control Conference, Publisher: IEEE
In this paper, a distributed method for faultdetection using sensor networks is proposed. Each sensorcommunicates only with neighboring nodes to compute locallyan estimate of the state of the system to monitor. A residualis defined and suitable stochastic thresholds are designed,allowing to set the parameters so to guarantee a maximumfalse alarms probability. The main novelty and challenge ofthe proposed approach consists in addressing the individualcorrelations between the state, the measurements, and thenoise components, thus significantly generalising the estimationmethodology compared to previous results. No assumptions onthe probability distribution family are needed for the noisevariables. Simulation results show the effectiveness of theproposed method, including an extensive sensitivity analysiswith respect to fault magnitude and measurement noise.
Boem F, Ferrari RMG, Keliris C, et al., 2017, A distributed networked approach for fault detection of large-scale systems, IEEE Transactions on Automatic Control, Vol: 62, Pages: 18-33, ISSN: 0018-9286
Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique.
Raimondo DM, Boem F, Gallo A, et al., 2016, A decentralized fault-tolerant control scheme based on Active Fault Diagnosis, 2016 IEEE 55th Conference on Decision and Control, Publisher: IEEE
This paper deals with a decentralized fault-tolerant control methodology based on an Active Fault Diagnosis approach. The proposed technique addresses the important problem of monitoring interconnected Large-Scale Systems (LSS). The fault diagnosis approach is made of a passive set-based fault detection method and an active fault isolation technique, able to guarantee isolability subject to local input and state constraints. The proposed scheme can be implemented locally in a decentralized way. A significant feature is the decentralized design constructed on tube-based Model Predictive Control to possibly allow the disconnection of faulty subsystems or the reconfiguration of local controllers. The Active Fault Diagnosis tool is designed to support the decision-making process for the control and monitoring of the LSS.
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