Publications
399 results found
Zoppoli R, Sanguineti M, Gnecco G, et al., 2020, Some families of FSP functions and their properties, Communications and Control Engineering, Pages: 89-150
We report properties of fixed-structure parametrized (FSP) functions that give insights into the effectiveness of the “Extended Ritz Method” (ERIM) as a methodology for the approximate solution of infinite-dimensional optimization problems. First, we present the structure of some widespread FSP functions, including linear combinations of fixed-basis functions, one-hidden-layer (OHL) and multiple-hidden-layer (MHL) networks, and kernel smoothing models. Second, focusing on the case of OHL neural networks based on ridge and radial constructions, we report their density properties under different metrics. Third, we present rates of function approximation via ridge OHL neural networks, by reporting a fundamental theorem by Maurey, Jones, and Barron, together with its extensions, based on a norm tailored to approximation by computational units from a given set of functions. We also discuss approximation properties valid for MHL networks. Fourth, we compare the classical Ritz method and the ERIM from the point of view of the curse of dimensionality, proving advantages of the latter for a specific class of problems, where the functional to be optimized is quadratic. Finally, we provide rates of approximate optimization by the ERIM, based on the concepts of modulus of continuity and modulus of convexity of the functional to be optimized.
Zoppoli R, Sanguineti M, Gnecco G, et al., 2020, Design of mathematical models by learning from data and FSP functions, Communications and Control Engineering, Pages: 151-206
First, well-known concepts from Statistical Learning Theory are reviewed. In reference to the problem of modelling an unknown input/output (I/O) relationship by fixed-structure parametrized functions, the concepts of expected risk, empirical risk, and generalization error are described. The last error is then split into approximation and estimation errors. Four quantities of interest are emphasized: the accuracy, the number of arguments of the I/O relationship, the model complexity, and the number of samples generated for the estimation. The possibility of generating such samples by deterministic algorithms like quasi-Monte Carlo methods, orthogonal arrays, Latin hypercubes, etc. gives rise to the so-called Deterministic Learning Theory. This possibility is an intriguing alternative to the random generation of input data, typically obtained by using Monte Carlo techniques, since it enables one to reduce the number of samples (under the same accuracy) and to obtain upper bounds on the errors in deterministic terms rather than in probabilistic ones. Deterministic learning relies on some basic quantities such as variation and discrepancy. Special families of deterministic sequences called “low-discrepancy sequences” are useful in the computation of integrals and in dynamic programming, to mitigate the danger of incurring the curse of dimensionality deriving from the use of regular grids.
Zoppoli R, Sanguineti M, Gnecco G, et al., 2020, Numerical methods for integration and search for minima, Communications and Control Engineering, Pages: 207-253
Two topics are addressed. The first refers to the numerical computation of integrals and expected values of functions that may depend on a large number of random variables. Of course, integration includes the computation of the expected values of functions dependent on random variables. However, the latter shows peculiar nontrivial aspects that the former does not have. In case of a large number of random variables, the use of regular grids implies the risk of incurring the curse of dimensionality. Then, suitable sampling methods are taken into account to reduce such risk. In particular, Monte Carlo and quasi-Monte Carlo sequences are addressed. The second topic refers to the solution of the nonlinear programming problems obtained from the approximation of infinite-dimensional optimization problems by the Extended Ritz Method. We mention a few well-known direct techniques and gradient-based descent algorithms. In the case of nonlinear programming problems stated in stochastic frameworks, the stochastic approximation approach deserves attention and thus it is considered in some detail. Within this context, we describe the stochastic gradient algorithm enabling one to avoid the computation of integrals, hence, the computation of expected values of functions dependent on random variables. Convergence properties of that algorithm are reported.
Barboni A, Parisini T, 2020, Towards Distributed Accommodation of Covert Attacks in Interconnected Systems, 59th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 5731-5736, ISSN: 0743-1546
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- Citations: 2
Rezaee H, Parisini T, Polycarpou MM, 2020, Control of Vehicular Platoons: Stochastic Robustness Against Jamming Attacks, 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, Publisher: ELSEVIER, Pages: 17041-17046, ISSN: 2405-8963
Pin G, Yang G, Serrani A, et al., 2020, Fixed-time Observer Design for LTI Systems by Time-varying Coordinate Transformation, 59th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 6040-6045, ISSN: 0743-1546
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- Citations: 1
Pin G, Wang Y, Serrani A, et al., 2020, Dynamic Certainty Equivalence Adaptive Control by Nonlinear Parameter Filtering, 59th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 1454-1459, ISSN: 0743-1546
Pin G, Chen B, Fedele G, et al., 2020, Robust Frequency-Adaptive PLL with Lyapunov Stability Guarantees, 4th IEEE Conference on Control Technology and Applications (IEEE CCTA), Publisher: IEEE, Pages: 498-503
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- Citations: 2
Gallo AJ, Barboni A, Parisini T, 2020, On detectability of cyber-attacks for large-scale interconnected systems, 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, Publisher: ELSEVIER, Pages: 3521-3526, ISSN: 2405-8963
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- Citations: 2
Fedele G, Pin G, Parisini T, 2020, A modified non-adaptive OSG-SOGI filter for estimation of a biased sinusoidal signal with global convergence properties, 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, Publisher: ELSEVIER, Pages: 530-535, ISSN: 2405-8963
Yang G, Rezaee H, Parisini T, 2020, Distributed State Estimation for a Class of Jointly Observable Nonlinear Systems, 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, Publisher: ELSEVIER, Pages: 5045-5050, ISSN: 2405-8963
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- Citations: 8
Zhang K, Polycarpou MM, Parisini T, 2020, Enhanced Anomaly Detector for Nonlinear Cyber-Physical Systems against Stealthy Integrity Attacks, 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, Publisher: ELSEVIER, Pages: 13682-13687, ISSN: 2405-8963
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- Citations: 5
Al-Dabbagh AW, Barboni A, Parisini T, 2020, Distributed Detection and Isolation of Covert Cyber Attacks for a Class of Interconnected Systems, 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, Publisher: ELSEVIER, Pages: 772-777, ISSN: 2405-8963
Barboni A, Gallo AJ, Boem F, et al., 2019, A Distributed Approach for the Detection of Covert Attacks in Interconnected Systems with Stochastic Uncertainties, 58th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 5623-5628, ISSN: 0743-1546
Barrere Cambrun M, Hankin C, Nicolaou N, et al., 2019, MaxSAT Evaluation 2019 - Benchmark: Identifying Security-Critical Cyber-Physical Components in Weighted AND/OR Graphs, MaxSAT Evaluation 2019 (affiliated with SAT 2019), Pages: 32-33
This paper presents a MaxSAT benchmark focused on identifying critical nodes in AND/OR graphs. We use AND/OR graphs to model Industrial Control Systems (ICS) as they are able to semantically grasp intricate logical interdependencies among ICS components. However, identifying critical nodes in AND/OR graphs is an NP-complete problem. We address this problem by efficiently transforming the input AND/OR graph-based model into a weighted logical formula that is then used to build and solve a Weighted Partial MaxSAT problem. The benchmark includes 80 cases with AND/OR graphs of different size and composition as well as the optimal cost and solution for each case.
Barrère M, Hankin C, Eliades DG, et al., 2019, Assessing cyber-physical security in industrial control systems, 6th International Symposium for ICS & SCADA Cyber Security Research 2019, Publisher: BCS Learning & Development, Pages: 49-58
Over the last years, Industrial Control Systems (ICS) have become increasingly exposed to a wide range of cyber-physical threats. Efficient models and techniques able to capture their complex structure and identify critical cyber-physical components are therefore essential. AND/OR graphs have proven very useful in this context as they are able to semantically grasp intricate logical interdependencies among ICS components. However, identifying critical nodes in AND/OR graphs is an NP-complete problem. In addition, ICS settings normally involve various cyber and physical security measures that simultaneously protect multiple ICS components in overlapping manners, which makes this problem even harder. In this paper, we present an extended security metric based on AND/OR hypergraphs which efficiently identifies the set of critical ICS components and security measures that should be compromised, with minimum cost (effort) for an attacker, in order to disrupt the operation of vital ICS assets. Our approach relies on MAX-SAT techniques, which we have incorporated in META4ICS, a Java-based security metric analyser for ICS. We also provide a thorough performance evaluation that shows the feasibility of our method. Finally, we illustrate our methodology through a case study in which we analyse the security posture of a realistic Water Transport Network (WTN).
Pin G, Chen B, Parisini T, 2019, Robust deadbeat continuous-time observer design based on modulation integrals, Automatica, Vol: 107, Pages: 95-102, ISSN: 0005-1098
In this paper, the state estimation problem of linear continuous-time systems is dealt with by a non-asymptotic state observer,which allows the state estimation error to decay within an arbitrarily-small finite time without resorting to high-gaininjection.By processing the measured input and output signals throughmodulation integrals, a number of auxiliary signals not affectedby the initial conditions are obtained, from which the system state can be computed by simple algebra. The problem of internalinstability of modulation integrals is addressed by resorting to a periodic rescaling mechanism that prevents error accumulationand singularities. We show that the combination of modulation integrals with periodic rescaling can be implemented as ajump-linear system. The robustness of the devised method with respect to additive measurement perturbations on the system’sinput/output is characterized by Input-to-State Stability arguments.
Chen B, Li P, Pin G, et al., 2019, Finite-time estimation of multiple exponentially-damped sinusoidal signals: A kernel-based approach, Automatica, Vol: 106, Pages: 1-7, ISSN: 0005-1098
The problem of estimating the parameters of biased and exponentially-damped multi-sinusoidal signals is addressed in this paper by a finite-time identification scheme based on Volterra integral operators. These parameters are the amplitudes, frequencies, initial phase angles, damping factors and the offset. The proposed strategy entails the design of a new kind of kernel function that, compared to existing ones, allows for the identification of the initial conditions of the signal-generator system. The worst-case behavior of the proposed algorithm in the presence of bounded additive disturbances is fully characterized by Input-to-State Stability arguments. Numerical examples including the comparisons with some existing tools are reported to 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.
Barrère M, Hankin C, Nicolau N, et al., 2019, Identifying security-critical cyber-physical components in industrial control systems, Publisher: arxiv
In recent years, Industrial Control Systems (ICS) have become an appealingtarget for cyber attacks, having massive destructive consequences. Securitymetrics are therefore essential to assess their security posture. In thispaper, we present a novel ICS security metric based on AND/OR graphs thatrepresent cyber-physical dependencies among network components. Our metric isable to efficiently identify sets of critical cyber-physical components, withminimal cost for an attacker, such that if compromised, the system would enterinto a non-operational state. We address this problem by efficientlytransforming the input AND/OR graph-based model into a weighted logical formulathat is then used to build and solve a Weighted Partial MAX-SAT problem. Ourtool, META4ICS, leverages state-of-the-art techniques from the field of logicalsatisfiability optimisation in order to achieve efficient computation times.Our experimental results indicate that the proposed security metric canefficiently scale to networks with thousands of nodes and be computed inseconds. In addition, we present a case study where we have used our system toanalyse the security posture of a realistic water transport network. We discussour findings on the plant as well as further security applications of ourmetric.
Li P, Pin G, Fedele G, et al., 2019, Deadbeat Source Localization from Range-only Measurements: a Robust Kernel-based Approach, IEEE Transactions on Control Systems Technology, Vol: 27, Pages: 923-933, ISSN: 1063-6536
This paper presents a novel framework for the problem of target localization based on the range information collected by a single mobile agent. The proposed methodology exploits the algebra of Volterra integral operators to annihilate the influence of initial conditions on the transient phase, thus achieving a deadbeat performance. The robustness properties against additive measurement perturbations are analyzed, and the bias caused by the time discretization is characterized as well. Extensive simulation results and comparisons are provided showing the effectiveness of the proposed technique in coping with both stationary and drifting targets.
Pin G, Wang Y, Chen B, et al., 2019, Identification of multi-sinusoidal signals with direct frequency estimation: an adaptive observer approach, Automatica, Vol: 99, Pages: 338-345, ISSN: 0005-1098
This paper addresses the problem of estimating the frequencies, amplitudes and phases of thensinusoidal components of apossibly biased multi-sinusoidal signal. The proposed adaptive observer allows thedirectadaptation of the frequency estimateswith a relatively low dynamic order 3n+ 1 (3nfor an unbiased signal). The stability analysis proves the global exponentialconvergence of the estimation error and the robustness to additive norm-bounded measurement perturbations.
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, 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.
Wang Y, Pin G, Serrani A, et al., 2019, Switching-based Rejection of an Unknown Harmonic Disturbance in Uncertain Stable Linear Systems under Measurement Noise, American Control Conference (ACC), Publisher: IEEE, Pages: 3020-3025, ISSN: 0743-1619
Yang G, Rezaee H, Parisini T, 2019, Sensor Redundancy for Robustness in Nonlinear State Estimation, 58th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 3865-3870, ISSN: 0743-1546
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- Citations: 1
Wang Y, Pin G, Serrani A, et al., 2019, Switching-based Rejection of Multi-sinusoidal Disturbance in Uncertain Stable Linear Systems under Measurement Noise, 58th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 6112-6117, ISSN: 0743-1546
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- Citations: 2
Pin G, Chen B, Fedele G, et al., 2019, Globally-stable tracking and estimation for single-phase electrical signals with DC-offset rejection, 45th Annual Conference of the IEEE Industrial Electronics Society (IECON), Publisher: IEEE, Pages: 4663-4668, ISSN: 1553-572X
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- Citations: 2
Wang Y, Serrani A, Pin G, et al., 2019, Switching-based Regulation of Uncertain Stable Linear Systems Affected by an Unknown Harmonic Disturbance, 8th International-Federation-of-Automatic-Control (IFAC) Symposium on Mechatronic Systems (MECHATRONICS) / 11th International-Federation-of-Automatic-Control (IFAC) Symposium on Nonlinear Control Systems (NOLCOS), Publisher: ELSEVIER, Pages: 604-609, ISSN: 2405-8963
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