Publications
399 results found
Jiang J, Astolfi A, Parisini T, 2019, Traffic Wave Damping: A Shared Control Approach, American Control Conference (ACC), Publisher: IEEE, Pages: 4860-4865, ISSN: 0743-1619
Barboni A, Rezaee H, Boem F, et al., 2019, Distributed Detection of Covert Attacks for Interconnected Systems, 18th European Control Conference (ECC), Publisher: IEEE, Pages: 2240-2245
- Author Web Link
- Cite
- Citations: 11
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.
Chen B, Pin G, Ng WM, et al., 2018, Online detection of fundamental and interharmonics in AC mains for parallel operation of multiple grid-connected power converters, IEEE Transactions on Power Electronics, Vol: 33, Pages: 9318-9330, ISSN: 0885-8993
Parallel operation of multiple grid-connected power converters through LCL filters is known to have the potential problem of triggering oscillations in the ac mains. Such oscillatory frequencies are not integral multiples of the fundamental frequency and hence form a new source of interharmonics. Early detection of such oscillations is essential for the parallel power converters to move out of the unstable zone. This paper presents an online observer-based algorithm that can perform fast detection of interharmonics within a specified frequency band. The algorithm has been adopted in a specific and reduced form from an integral observer algorithm for detection of fundamental and interharmonic voltage components in the ac mains. A new method based on the kernel signal for fast interharmonic detection is proposed and practically verified. It has been implemented in a digital controller to detect oscillations such as those occurring between two grid-connected power converters. The practical results indicate that the algorithm can locate such frequency within the specific frequency band within 1 mains cycle.
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.
Khalili M, Zhang X, Cao Y, et al., 2018, Distributed adaptive fault-tolerant leader-following formation control of nonlinear uncertain second-order multi-agent systems, International Journal of Robust and Nonlinear Control, Vol: 28, Pages: 4287-4308, ISSN: 1049-8923
This paper presents a distributed integrated fault diagnosis and accommodation scheme for leader‐following formation control of a class of nonlinear uncertain second‐order multi‐agent systems. The fault model under consideration includes both process and actuator faults, which may evolve abruptly or incipiently. The time‐varying leader communicates with a small subset of follower agents, and each follower agent communicates to its directly connected neighbors through a bidirectional network with possibly asymmetric weights. A local fault diagnosis and accommodation component are designed for each agent in the distributed system, which consists of a fault detection and isolation module and a reconfigurable controller module comprised of a baseline controller and two adaptive fault‐tolerant controllers, activated after fault detection and after fault isolation, respectively. By using appropriately the designed Lyapunov functions, the closed‐loop stability and asymptotic convergence properties of the leader‐follower formation are rigorously established under different modes of the fault‐tolerant control system.
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
Khalili M, Zhang X, Cao Y, et al., 2018, Distributed Fault-Tolerant Control of High-Order Input-Output Multi-Agent Systems, 10th International-Federation-of-Automatic-Control (IFAC) Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Publisher: ELSEVIER SCIENCE BV, Pages: 453-458, 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.
Ascencio P, Astolfi A, Parisini T, 2018, Backstepping PDE design: a convex optimization approach, IEEE Transactions on Automatic Control, Vol: 63, Pages: 1943-1958, ISSN: 0018-9286
Backstepping design for boundary linear PDE is formulated as a convex optimization problem. Some classes of parabolic PDEs and a first-order hyperbolic PDE are studied, with particular attention to non-strict feedback structures. Based on the compactness of the Volterra and Fredholm-type operators involved, their Kernels are approximated via polynomial functions. The resulting Kernel-PDEs are optimized using Sum-of-Squares (SOS) decomposition and solved via semidefinite programming, with sufficient precision to guarantee the stability of the system in the L2-norm. This formulation allows optimizing extra degrees of freedom where the Kernel-PDEs are included as constraints. Uniqueness and invertibility of the Fredholm-type transformation are proved for polynomial Kernels in the space of continuous functions. The effectiveness and limitations of the approach proposed are illustrated by numerical solutions of some Kernel-PDEs.
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.
Li P, Pin G, Fedele G, et al., 2018, Non-asymptotic numerical differentiation: a kernel-based approach, International Journal of Control, Vol: 91, Pages: 2090-2099, ISSN: 0020-7179
The derivative estimation problem is addressed in this paper by using Volterra integral operators which allow to obtain the estimates of the time-derivatives with fast convergence rate. A deadbeat state observer is used to provide the estimates of the derivatives with a given fixed-time convergence. The estimation bias caused by modeling error is characterized herein as well as the ISS property of the estimation error with respect to the measurement perturbation. A number of numerical examples are carried out to show the effectiveness of the proposed differentiator also including comparisons with some existing methods.
Chen B, Pin G, Ng WM, et al., 2018, An Adaptive-Observer-Based Robust Estimator of Multi-sinusoidal Signals, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 63, Pages: 1618-1631, ISSN: 0018-9286
This paper presents an adaptive observer-based robust estimation methodology of the amplitudes, frequencies and phases of biased multi-sinusoidal signals in presence of bounded perturbations on the measurement. The parameters of the sinusoidal components are estimated on-line and the update laws are individually controlled by an excitation-based switching logic enabling the update of a parameter only when the measured signal is sufficiently informative. This way doing, the algorithm is able to tackle the problem of over-parametrization (i.e., when the internal model accounts for a number of sinusoids that is larger than the true spectral content) or temporarily fading sinusoidal components. The stability analysis proves the existence of a tuning parameter set for which the estimator's dynamics are input-to-state stable with respect to bounded measurement disturbances. The performance of the proposed estimation approach is evaluated and compared with other existing tools by extensive simulation trials and real-time experiments.
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.
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.
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
- Author Web Link
- Cite
- Citations: 2
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
- Author Web Link
- Cite
- Citations: 1
Keliris C, Polycarpou MM, Parisini T, 2018, An Adaptive Approach to Sensor Bias Fault Diagnosis and Accommodation for a Class of Input-Output Nonlinear Systems, 57th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 6334-6339, ISSN: 0743-1546
- Author Web Link
- Cite
- Citations: 5
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
- Author Web Link
- Cite
- Citations: 4
Wang Y, Pin G, Serrani A, et al., 2018, Switching-based Sinusoidal Disturbance Rejection for Uncertain Stable Linear Systems, American Control Conference, Publisher: IEEE, Pages: 4502-4507, ISSN: 0743-1619
- Author Web Link
- Cite
- Citations: 6
Mehammer EB, Fore M, Sauder T, et al., 2018, Kalman estimation of position and velocity for ReaTHM testing applications, 15th Deep Sea Offshore Wind R and D Conference (EERA DeepWind), Publisher: IOP PUBLISHING LTD, ISSN: 1742-6588
- Author Web Link
- Cite
- Citations: 1
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, 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
- Author Web Link
- Cite
- Citations: 1
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
- Author Web Link
- Cite
- Citations: 4
Khalili M, Zhang X, Polycarpou M, et al., 2017, Distributed AdaptiveFault-Tolerant Control of Uncertain Multi-Agent Systems, Automatica, Vol: 87, Pages: 142-151, ISSN: 0005-1098
This brief paper presents a distributed adaptive fault-tolerant leader-following consensus control scheme for a class of nonlinear uncertain multi-agent systems under a bidirectional communication topology with possibly asymmetric weights and subject to process and actuator faults. A local fault-tolerant control (FTC) component is designed for each agent using local measurements and suitable information exchanged between neighboring agents. Each local FTC component consists of a fault diagnosis module and a reconfigurable controller module comprised of a baseline controller and two adaptive fault-tolerant controllers activated after fault detection and after fault isolation, respectively. By using an appropriately chosen Lyapunov function, the closed-loop stability and asymptotic convergence property of leader–follower consensus are rigorously established under different operating modes of the FTC system.
Pin G, Li P, Fedele G, et al., 2017, A Deadbeat Observer for LTI Systems by Time/Output-Dependent State Mapping, IEEE 56th Annual Conference on Decision and Control (CDC), Publisher: IEEE, ISSN: 0743-1546
Khalili M, Zhang X, Cao Y, et al., 2017, Distributed Adaptive Fault-Tolerant Control of a Class of High-Order Nonlinear Uncertain Multi-Agent Systems, IEEE 56th Annual Conference on Decision and Control (CDC), Publisher: IEEE, ISSN: 0743-1546
Fedele G, D'Alfonso L, Pin G, et al., 2017, Volterra's kernels-based finite-time parameters estimation of the Chua system, 2nd International Conference on Numerical Computations - Theory and Algorithms (NUMTA), Publisher: Elsevier, Pages: 121-130, ISSN: 0096-3003
In this work, the unknown set of parameters of the Chua system is recovered under the hypothesis that the voltages of the capacitors are available. The system is shown to be algebraically observable and identifiable with respect to the chosen outputs. Focusing on the differential equations, the Volterra kernel-based approach is used to perform an estimation without the uncertainty of the unmeasurable derivatives and the unknown initial conditions.
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.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.