Publications
399 results found
Pin G, Li P, Fedele G, et al., 2017, A Deadbeat Observer for Two and Three-dimensional LTI Systems by a Time/Output-Dependent State Mapping, 20th World Congress of the International-Federation-of-Automatic-Control (IFAC), Publisher: ELSEVIER SCIENCE BV, Pages: 6452-6457, ISSN: 2405-8963
Wang Y, Chen B, Pin G, et al., 2017, Estimation of Damped Sinusoidal Signals: an Observer-Based Approach, 20th World Congress of the International-Federation-of-Automatic-Control (IFAC), Publisher: ELSEVIER SCIENCE BV, Pages: 3811-3816, ISSN: 2405-8963
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.
Parisini T, Pin G, Chen B, 2017, Robust finite-time estimation of biased sinusoidal signals: a volterra operators approach, Automatica, Vol: 77, Pages: 120-132, ISSN: 0005-1098
A novel finite-time convergent estimation technique is proposed for identifying the amplitude, frequency and phase of abiasedsinusoidal signal. Resorting to Volterra integral operators with suitably designed kernels, the measured signal is processedyielding a set of auxiliary signals in which the influence of the unknown initial conditions is removed. A second-order slidingmode-based adaptation law – fed by the aforementioned auxiliary signals – is designed for finite-time estimation of the frequency,amplitude, and phase. The worst case behavior of the proposed algorithm in presence of the bounded additive disturbancesis fully characterized by Input-to-State Stability arguments. The effectiveness of the estimation technique is evaluated andcompared with other existing tools via extensive numericalsimulations.
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.
Boem F, Carli R, Farina M, et al., 2016, Scalable monitoring of interconnected stochastic systems, 2016 IEEE 55th Conference on Decision and Control, Publisher: IEEE
In this paper, we propose a novel distributed faultdetection method to monitor the state of a linear system, par-titioned into interconnected subsystems. The approach hingeson the definition of a partition-based distributed Luenbergerestimator, based on the local model of the subsystems andthat takes into account the dynamic coupling terms betweenthe subsystems. The proposed methodology computes –in adistributed way– a bound on the variance of a properly definedresidual signal, considering the uncertainty related to thestate estimates performed by the neighboring subsystems. Thisbound allows the computation of suitable local thresholds withguaranteed maximum false-alarms rate. The implementationof the proposed estimation and fault detection method isscalable, allowingPlug & Playoperations and the possibilityto disconnect the faulty subsystem after fault detection. Theo-retical conditions guaranteeing the convergence of the estimatesand of the bounds are provided. Simulation results show theeffectiveness of the proposed method.
Ascencio P, Astolfi A, Parisini T, 2016, Backstepping PDE-based adaptive observer for a single particle model of lithium-ion batteries, 55th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 5623-5628, ISSN: 0743-1546
This article deals with the observer design problem for the simultaneous estimation of the solid Lithium concentration and of the diffusion parameter for a Single Particle Model of Lithium-Ion Batteries. The design is based on the Backstepping PDE methodology, including a modified Volterra transformation to compensate for the diffusivity uncertainty. The resulting coupled/uncoupled Kernel-PDE and Ordinary Differential Equation (ODE) are recast, via a Sum-of-Squares decomposition, in terms of a convex optimization problem and solved by semidefinite programming, allowing, at each fixed time, an efficient computation of the state and parameter observer gains. In addition, based on the Moment approach, a novel scheme of inversion of the nonlinear output mapping of the Single Particle Model is presented. The effectiveness of this approach is illustrated by numerical simulations.
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.
Wang Y, Pin G, Serrani A, et al., 2016, Removing SPR-like conditions in Adaptive Feedforward Control of uncertain systems, Pages: 4728-4733
This paper presents the current developments of a novel approach to robust Adaptive Feedforward Control (AFC) of uncertain linear systems affected by harmonic disturbance of known frequency. The features that set the proposed method apart from existing ones are the following: (i) knowledge of the sign of both the real and imaginary parts of the transfer function at the frequency of excitation is not needed; (ii) persistence of excitation is not required; (iii) stability analysis tools based on averaging are avoided, hence the requirement of an exponentially stable equilibrium is circumvented. The methodology reposes upon recent results on adaptive regulation of uncertain linear systems with weak immersions as well as on classic tools in adaptive control. A noticeable drawback of the proposed controller - at this stage of the research - is its relatively high dimensionality, which stems from the necessity of a convexification of a non-convex parameter set.
Parisini T, 2016, Editorial, IEEE Transactions on Control Systems Technology, Vol: 25, Pages: 1-2, ISSN: 1558-0865
With this last Editorial, I’m very pleased to welcome the new Editor in Chief of the IEEE Transactions on Control Systems Technology, Prof. Andrea Serrani, effective January 1, 2017. Prof. Serrani received the Laurea (B.Eng.) degree in electrical engineering (summa cum laude) and the Ph.D. degree in artificial intelligence systems from the University of Ancona, Italy, in 1993 and 1997, respectively, and the M.S. and D.Sc. degrees in systems science and mathematics from Washinton University in St. Louis, MI, USA, in 1996 and 2000, respectively. From 1994 to 1999, he was a Fulbright Fellow at Washington University in St. Louis. Since 2002, he has been with the Department of Electrical and Computer Engineering at The Ohio State University, where he is currently a Professor and Chair of Graduate Studies. Between 2004 and 2007, he served as the technical leader for the Reusable Launch Vehicle area of the AFOSR/AFRL Collaborative Center for Control Sciences at The Ohio State University. He has held visiting positions at the Center for Research on Complex Automated Systems of the University of Bologna and at the University of Padua, Italy, and multiple summer faculty positions at the Air Force Research Laboratory, including two AF-ASEE Fellowships. Among other editorial positions, Andrea served as Associate Editor of these Transactions since 2010. Prof. Serrani has an outstanding research track record both on theory and applications. He is simply a perfect choice for this position.
Riverso S, Boem F, Ferrari-Trecate G, et al., 2016, Plug-and-Play Fault Detection and control-reconfiguration for a class of nonlinear large-scale constrained systems, IEEE Transactions on Automatic Control, Vol: 61, Pages: 3963-3978, ISSN: 0018-9286
This paper deals with a novel Plug-and-Play (PnP) architecture for the control and monitoring of Large-Scale Systems (LSSs). The proposed approach integrates a distributed Model Predictive Control (MPC) strategy with a distributed Fault Detection (FD) architecture and methodology in a PnP framework. The basic concept is to use the FD scheme as an autonomous decision support system: once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. PnP design of local controllers and detectors allow these operations to be performed safely, i.e. without spoiling stability and constraint satisfaction for the whole LSS. The PnP distributed MPC is derived for a class of nonlinear LSSs and an integrated PnP distributed FD architecture is proposed. Simulation results in two paradigmatic examples show the effectiveness and the potential of the general methodology.
Chiaravalloti F, D'Alfonso L, D'Aquila G, et al., 2016, Finite-time parameters estimation of the Chua system, 2nd International Conference on Numerical Computations - Theory and Algorithms (NUMTA), Publisher: American Institute of Physics Publising LLC, ISSN: 1551-7616
In this work, the unknown set of parameters of the Chua system is recovered under the hypothesys that the voltages of the capacitors are available. To this end, 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.
Chen B, Pin G, Ng WM, et al., 2016, A fast-convergent modulation integral observer for online detection of the fundamental and harmonics in grid-connected power electronics systems, IEEE Transactions on Power Electronics, Vol: 32, Pages: 2596-2607, ISSN: 1941-0107
Harmonics detection is a critical element of activepower filters. A previous review has shown that the RecursiveDiscrete Fourier Transform and the Instantaneous p-q Theory areeffective solutions to extracting power harmonics in single-phaseand three-phase power systems, respectively. This paper presentsthe operating principle of a new modulation function integralobserver algorithm that offers a fast solution for the extraction ofthe fundamental current and the total harmonic current whencompared with existing methods. The proposed method can beapplied to both single- and three-phase systems. The observerbasedalgorithm has an advantageous feature of being able to betuned offline for a specific application, having fast convergenceand producing estimated fundamental component with highcircularity. It has been tested with both simulations and practicalmeasurements for extracting the total harmonic current in ahighly efficient manner. The results have confirmed that theproposed tool offers a new and highly effective alternative to thesmart grid industry.
Parisini T, 2016, Editorial, IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, Vol: 24, Pages: 381-382, ISSN: 1063-6536
This editorial aims to share with our readership the status and a few figures about our TRANSACTIONS.
Keliris C, Polycarpou MM, Parisini T, 2016, An integrated learning and filtering approach for fault diagnosis of a class of nonlinear dynamical systems, IEEE Transactions on Neural Networks and Learning Systems, Vol: 28, Pages: 988-1004, ISSN: 2162-237X
This paper develops an integrated filtering and adaptive approximation-based approach for fault diagnosis of process and sensor faults in a class of continuous-time nonlinear systems with modeling uncertainties and measurement noise. The proposed approach integrates learning with filtering techniques to derive tight detection thresholds, which is accomplished in two ways: 1) by learning the modeling uncertainty through adaptive approximation methods and 2) by using filtering for dampening measurement noise. Upon the detection of a fault, two estimation models, one for process and the other for sensor faults, are initiated in order to identify the type of fault. Each estimation model utilizes learning to estimate the potential fault that has occurred, and adaptive isolation thresholds for each estimation model are designed. The fault type is deduced based on an exclusion-based logic, and fault detectability and identification conditions are rigorously derived, characterizing quantitatively the class of faults that can be detected and identified by the proposed scheme. Finally, simulation results are used to demonstrate the effectiveness of the proposed approach.
Pin G, Assalone A, Lovera M, et al., 2016, Non-asymptotic kernel-based parametric estimation of continuous-time linear systems, IEEE Transactions on Automatic Control, Vol: 61, Pages: 360-373, ISSN: 0018-9286
In this paper, a novel framework to address the problem of parametric estimation for continuous-time linear time-invariant dynamic systems is dealt with. The proposed methodology entails the design of suitable kernels of non-anticipative linear integral operators thus obtaining estimators showing, in the ideal case, “non-asymptotic” (i.e., “finite-time”) convergence. The analysis of the properties of the kernels guaranteeing such a convergence behaviour is addressed and a novel class of admissible kernel functions is introduced. The operators induced by the proposed kernels admit implementable (i.e., finite-dimensional and internally stable) state-space realizations. Extensive numerical results are reported to show the effectiveness of the proposed methodology. Comparisons with some existing continuous-time estimators are addressed as well and insights on the possible bias affecting the estimates are provided.
Ascencio P, Astolfi A, Parisini T, 2016, An Adaptive Observer for a class of Parabolic PDEs based on a Convex Optimization Approach for Backstepping PDE Design, American Control Conference (ACC), Publisher: IEEE, Pages: 3429-3434, ISSN: 0743-1619
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- Citations: 4
Li P, Pin G, Parisini T, et al., 2016, Deadbeat Source Localization from Range-only Measurements: a Robust Kernel-based Approach, American Control Conference (ACC), Publisher: IEEE, Pages: 2729-2734, ISSN: 0743-1619
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- Citations: 5
Chen B, Parisini T, Polycarpou MM, 2016, A Deadbeat Estimator-Based Fault Isolation Scheme for Nonlinear Systems, European Control Conference (ECC), Publisher: IEEE, Pages: 734-739
Chen B, Li P, Pin G, et al., 2016, Estimation of Multi-Sinusoidal Signals: A Deadbeat Methodology, 55th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 3763-3768, ISSN: 0743-1546
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- Citations: 3
Li P, Fedele G, Pin G, et al., 2016, Kernel-based Deadbeat Parametric Estimation of Bias-affected Damped Sinusoidal Signals, European Control Conference (ECC), Publisher: IEEE, Pages: 519-524
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- Citations: 6
Yin J, Lin D, Parisini T, et al., 2015, Front-End Monitoring of the Mutual Inductance and Load Resistance in a Series-Series Compensated Wireless Power Transfer System, IEEE Transactions on Power Electronics, Vol: 31, Pages: 7339-7352, ISSN: 1941-0107
In this paper, a new method to estimate the mutual inductance and load resistance in a series-series compensated wireless power transfer system is presented. Reasonably accurate estimations can be obtained from measurements of the input voltage and current obtained at one operating frequency only. The proposal can be used to dynamically monitor both the coupling relationship between the transmitter and receiver coils and the load conditions without any direct measurement on the receiver side. It can also be used as a simple method to measure the mutual inductance of any pair of coupled coils. A novel impedance spectrum analysis method is further presented to show that series-series compensation has special characteristics in its input impedance spectrum. Experimental results with acceptable tolerance are included to show the effectiveness of the proposed method.
Boem F, Riverso S, Ferrari-Trecate G, et al., 2015, Stochastic Fault Detection in a plug-and-play scenario, 2015 54th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 3137-3142, ISSN: 0743-1546
This paper proposes a novel stochastic Fault Detection (FD) approach for the monitoring of Large-Scale Systems (LSSs) in a Plug-and-Play (PnP) scenario. The proposed architecture considers stochastic bounds on the measurement noises and modeling uncertainties, providing probabilistic time-varying FD thresholds with guaranteed false alarms probability levels. The monitored LSS consists of several interconnected subsystems and the designed FD architecture is able to manage plugging-in of novel subsystems and un-plugging of existing ones. Moreover, the proposed PnP approach can perform the unplugging of faulty subsystems in order to avoid the propagation of faults in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. The reconfiguration processes involve only local operations of neighboring subsystems, thus allowing a scalable architecture. A consensus approach is used for the estimation of variables shared among more than one subsystem; a method is proposed to define the time-varying consensus weights in order to allow PnP operations and to minimize at each step the variance of the uncertainty of the FD thresholds. Simulation results on a Power Network application show the effectiveness of the proposed approach.
Boem F, Parisini T, 2015, Distributed model-based fault diagnosis with stochastic uncertainties, 2015 54th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 4474-4479, ISSN: 0743-1546
This paper proposes a novel distributed fault detection and isolation approach for the monitoring of non linear large-scale systems. The proposed architecture considers stochastic characterization of the measurement noises and modeling uncertainties, computing at each step stochastic time-varying thresholds with guaranteed false alarms probability levels. The convergence properties of the distributed estimation are demonstrated. A novel fault isolation method is proposed basing on a Generalized Observer Scheme, providing guaranteed error probabilities of the fault exclusion task. A consensus approach is used for the estimation of variables shared among more than one subsystem; a method is proposed to define the time-varying consensus weights in order to minimize at each step the variance of the uncertainty of the fault detection and isolation thresholds. Detectability and isolability conditions are provided.
Ferrari RMG, Boem F, Parisini T, 2015, An algebraic approach to modeling distributed multiphysics problems: The case of a DRI reactor, 4th IFAC Workshop on Mining, Mineral and Metal Processing MMM 2015, Publisher: Elsevier, Pages: 155-160, ISSN: 1474-6670
This paper deals with the problem of modelling a chemical reactor for the Direct Reduction of Iron ore (DRI). Such a process is being increasingly promoted as a more viable alternative to the classic Blast Furnace for the production of iron from raw minerals. Due to the inherent complexity of the process and the reactor itself, its effective monitoring and control requires advanced mathematical models containing distributed-parameter components. While classical approaches such as Finite Element or Finite Differences are still reasonable options, for accuracy and computational efficiency reasons, an algebraic approach is proposed. A full multi-physical, albeit one-dimensional model is addressed and its accuracy is analysed.
Boem F, Ferrari RMG, Parisini T, et al., 2015, Optimal topology for distributed fault detection of large-scale systems, 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015, Publisher: Elsevier, Pages: 60-65, ISSN: 1474-6670
The paper deals with the problem of defining the optimal topology for a distributed fault detection architecture for non-linear large-scale systems. A stochastic modelbased framework for diagnosis is formulated. The system structural graph is decomposed into subsystems and each subsystem is monitored by one local diagnoser. It is shown that overlapping of subsystems allows to improve the detectability properties of the monitoring architecture. Based on this theoretical result, an optimal decomposition design method is proposed, able to define the minimum number of detection units needed to guarantee the detectability of certain faults while minimizing the communication costs subject to some computation cost constraints. An algorithmic procedure is presented to solve the proposed optimal decomposition problem. Preliminary simulation results show the potential of the proposed approach.
Boem F, Riverso S, Ferrari-Trecate G, et al., 2015, A plug-and-play fault diagnosis approach for large-scale systems, 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015, Publisher: Elsevier, Pages: 601-606, ISSN: 1474-6670
This paper proposes a novel Plug-and-Play (PnP) dynamic approach for the monitoring of Large-Scale Systems (LSSs). The proposed architecture exploits a distributed Fault Detection and Isolation (FDI) methodology for nonlinear LSS in a PnP framework. The LSS consists of several interconnected subsystems and the designed FDI architecture is able to manage plugging-in of novel subsystems and un-plugging of existing ones. Moreover, the proposed PnP approach performs the unplugging of faulty subsystems in order to avoid the propagation of faults in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. The reconfiguration processes only involves local operations of neighboring subsystems, thus allowing a distributed architecture.
Khalili M, Zhang X, Polycarpou M, et al., 2015, Distributed adaptive fault-tolerant control of uncertain multi-agent systems, Pages: 66-71
This paper presents an adaptive fault-tolerant control (FTC) scheme for a class of nonlinear uncertain multi-agent systems. A local FTC scheme is designed for each agent using local measurements and suitable information exchanged between neighboring agents. Each local FTC scheme 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. Under certain assumptions, the closedloop system's stability and leader-follower consensus properties are rigorously established under different modes of the FTC system, including the time-period before possible fault detection, between fault detection and possible isolation, and after fault isolation.
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