Publications
399 results found
Rezaee H, Zhang K, Parisini T, et al., 2024, Cooperative adaptive cruise control in the presence of communication and radar stochastic data loss, IEEE Transactions on Intelligent Transportation Systems, Pages: 1-13, ISSN: 1524-9050
Control of a platoon of connected vehicles with nonlinear longitudinal dynamics subject to failure in receiving communicated and radio data is addressed in this paper. We consider a scenario when due to malfunction of communication and radio devices, required data for cooperative adaptive cruise control may be stochastically unavailable. We develop a control scheme such that under certain conditions, the regulation of the intervehicle distances in desired values can be guaranteed. Specifically, we rigorously show that if the probability of receiving the required data for each vehicle is not zero, then under the proposed control strategy, the intervehicle distances almost surely converge to the desired values. Accurate performance of radars is a key assumption in the existing results in the literature on cooperative adaptive cruise control. Hence, the main contribution of this paper is that under the proposed control strategy, the robustness of the platoon against stochastic data loss in both communication network and vehicles radars is guaranteed. Simulation results verify the acceptable performance of the proposed control strategy.
Rezaee H, Parisini T, Polycarpou MM, 2024, Leaderless cooperative adaptive cruise control based on constant time-gap spacing policy, IEEE Transactions on Automatic Control, Vol: 69, Pages: 659-666, ISSN: 0018-9286
Cooperative adaptive cruise control (CACC) in leaderless scenario is addressed in this article. The common idea in CACC is that a leader vehicle determines the steady-state speed of the platoon without considering feedback from other vehicles. The main contribution of this article is to propose a control strategy such that each vehicle in a leaderless platoon keeps a desired distance from its preceding vehicle, determined based on the constant time-gap spacing policy, and simultaneously, all the vehicles reach consensus upon their speeds. Moreover, whereas each vehicle needs access to the information of some neighboring vehicles (via a communication network) to achieve speed consensus, we assume that it has access to the relative distance of just one possible preceding vehicle (using a radar/lidar). Simulation results illustrate the performance of the proposed control strategy.
Azzollini IA, Bin M, Marconi L, et al., 2023, Robust and scalable distributed recursive least squares, Automatica, Vol: 158, ISSN: 0005-1098
We consider a problem of robust estimation over a network in an errors-in-variables context. Each agent measures noisy samples of a local pair of signals related by a linear regression defined by a common unknown parameter, and the agents must cooperate to find the unknown parameter in presence of uncertainty affecting both the regressor and the regressand variables. We propose a recursive least squares estimation method providing global exponential convergence to the unknown parameter in absence of uncertainty, and robust stability of the estimate, formalized in terms of input-to-state stability, in presence of uncertainty affecting all the variables. The result relies on a cooperative excitation assumption that is proved to be strictly weaker than persistency of excitation of each local data set. The proposed estimator is validated on an adaptive road pricing application.
Bin M, Parisini T, 2023, A small-gain theory for abstract systems on topological spaces, IEEE Transactions on Automatic Control, Vol: 68, Pages: 4494-4507, ISSN: 0018-9286
We develop a small-gain theory for systemsdescribed by set-valued maps between topological spaces.We introduce an abstract notion of stability unifying thecontinuity properties underlying different existing con cepts, such as Lyapunov stability of equilibria, sets, or mo tions, (incremental) input–output stability, asymptotic gainproperties, and continuity with respect to fast-switchinginputs. Then, we prove that a feedback interconnection en joying a given abstract small-gain property is stable. While,in general, the proposed small-gain property cannot bedecomposed as the union of stability of the subsystemsand a contractiveness condition, we show that it is impliedby standard assumptions in the context of input-to-statestable systems. Finally, we provide application examplesillustrating how the developed theory can be used for theanalysis of interconnected systems and design of controlsystems.
Dudkina E, Bin M, Breen J, et al., 2023, A comparison of centrality measures and their role in controlling the spread in epidemic networks, International Journal of Control, ISSN: 0020-7179
The ranking of nodes in a network according to their centrality or ``importance'' is a classic problem that has attracted the interest of different scientific communities in the last decades. The COVID-19 pandemic has recently rejuvenated the interest in this problem, as the ranking may be used to decide who should be tested, or vaccinated, first, in a population of asymptomatic individuals. In this paper, we review classic methods for node ranking and compare their performance in a benchmark network that considers the community-based structure of society. The outcome of the ranking procedure is then used to decide which individuals should be tested, and possibly quarantined, first. Finally, we also review the extension of these ranking methods to weighted graphs and explore the importance of weights in a contact network by providing a toy model and comparing node rankings for this case in the context of disease spread.
Zhang K, Keliris C, Parisini T, et al., 2023, Passive attack detection for a class of stealthy intermittent integrity attacks, IEEE/CAA Journal of Automatica Sinica, Vol: 10, Pages: 898-915, ISSN: 2329-9266
This paper proposes a passive methodology for detecting a class of stealthy intermittent integrity attacks in cyber-physical systems subject to process disturbances and measurement noise. A stealthy intermittent integrity attack strategy is first proposed by modifying a zero-dynamics attack model. The stealthiness of the generated attacks is rigorously investigated under the condition that the adversary does not know precisely the system state values. In order to help detect such attacks, a backward-in-time detection residual is proposed based on an equivalent quantity of the system state change, due to the attack, at a time prior to the attack occurrence time. A key characteristic of this residual is that its magnitude increases every time a new attack occurs. To estimate this unknown residual, an optimal fixed-point smoother is proposed by minimizing a piece-wise linear quadratic cost function with a set of specifically designed weighting matrices. The smoother design guarantees robustness with respect to process disturbances and measurement noise, and is also able to maintain sensitivity as time progresses to intermittent integrity attack by resetting the covariance matrix based on the weighting matrices. The adaptive threshold is designed based on the estimated backward-in-time residual, and the attack detectability analysis is rigorously investigated to characterize quantitatively the class of attacks that can be detected by the proposed methodology. Finally, a simulation example is used to demonstrate the effectiveness of the developed methodology.
Pin G, Chen B, Fedele G, et al., 2023, Robust frequency-adaptive quadrature phase-locked-loops with lyapunov-certified global stability, IEEE Transactions on Control Systems Technology, Vol: 31, Pages: 467-474, ISSN: 1063-6536
This work describes and compares two phase-locked-loop (PLL) algorithms aimed at tracking a biased sinusoidal signal with unknown frequency, amplitude, and phase, with inherent robustness to dc offset. The proposed methods endow quadrature PLLs, renowned for their excellent tracking performance, with frequency-adaptation capability, while providing robust global stability certificates. The large-gain global stability, proved by Lyapunov-like arguments borrowed from adaptive control theory, represents a major benefit when compared to the conventional PLLs, whose convergence instead can be proved only locally by small-signal analysis or small-gain assumptions. In this connection, the proposed algorithms represent the first frequency-adaptive and dc-bias rejecting PLL-type architectures with Lyapunov-certified global stability. When used for signal tracking, the proposed methods are shown to outperform the adaptive observer, especially in noisy conditions. Moreover, they provide more accurate frequency estimates than existent frequency-adaptive PLLs, showing enhanced robustness in facing both phase-noise and measurement perturbations.
Yang G, Rezaee H, Alessandri A, et al., 2023, State estimation using a network of distributed observers with switching communication topology, Automatica, Vol: 147, Pages: 1-11, ISSN: 0005-1098
State estimation of linear time-invariant (LTI) systems by using a network of distributed observers is studied in this paper. We assume that each observer has access to a local measurement which may be insufficient to provide the observability of the system, but the ensemble of all measurements in the network guarantees the observability. In this condition, the objective is to design a distributed state estimation approach such that, while the observers can exchange their estimated state vectors under a communication network, the estimated state vector of each observer converges to the state vector of the system. We consider a scenario when the communication links may fail and rebuild over time and the communication network does not stay connected constantly. Accordingly, the main contribution of the paper is to propose a distributed approach (with guarantees on the feasibility of the design) such that the state vector of the system is estimated by each observer if the union/joint of communication links in bounded intervals of time makes the network communication graph connected. Moreover, we also consider a scenario when the LTI system is subject to external disturbances and measurement noise. In this case, we derive sufficient conditions on the proposed approach such that if the communication topology stays connected during links failure, a desired performance to attenuate the effect of external disturbances and measurement noise on estimation errors is guaranteed. Simulation results show the effectiveness of the proposed estimation approach.
Rossiter JA, Cassandras CG, Hespanha J, et al., 2023, Control education for societal-scale challenges: A community roadmap, Annual Reviews in Control, Vol: 55, Pages: 1-17, ISSN: 1367-5788
This article focuses on extending, disseminating and interpreting the findings of an IEEE Control Systems Society working group looking at the role of control theory and engineering in solving some of the many current and future societal challenges. The findings are interpreted in a manner designed to give focus and direction to both future education and research work in the general control theory and engineering arena, interpreted in the broadest sense. The paper is intended to promote discussion in the community and also provide a useful starting point for colleagues wishing to re-imagine the design and delivery of control-related topics in our education systems, especially at the tertiary level and beyond.
Zhang K, Keliris C, Parisini T, et al., 2022, Stealthy integrity attacks for a class of nonlinear cyber-physical systems, IEEE Transactions on Automatic Control, Vol: 67, Pages: 6723-6730, ISSN: 0018-9286
This paper proposes a stealthy integrity attack generation methodology for a class of nonlinear cyber-physical systems. Geometric control theory and stability theory of incremental systems are used to design an attack generation scheme with stealthiness properties. An attack model is proposed as a closed-loop dynamical system with an arbitrary input signal. This model is developed based on a controlled invariant subspace that results from geometric control theory and is decoupled with the system outputs and the nonlinear function. The presence of the arbitrary signal in the attack model provides an additional degree of freedom and constitutes a novel component compared with existing results. The stealthiness property of the attack model is rigorously investigated based on the incremental stability of the closed-loop control system, and the incremental input-to-state stability of the anomaly detector. As a result, a sufficient condition in terms of the initial condition of the attack model is derived to guarantee stealthiness. Finally, a case study is presented to illustrate the effectiveness of the developed attack generation scheme.
Parisini T, 2022, Looking Back at a Two-Year Journey, IEEE CONTROL SYSTEMS MAGAZINE, Vol: 42, Pages: 8-11, ISSN: 1066-033X
Scandella M, Ghosh A, Bin M, et al., 2022, Traffic-light control in urban environment exploiting drivers' reaction to the expected red lights duration, Transportation Research Part C: Emerging Technologies, Vol: 145, ISSN: 0968-090X
Traffic congestion in urban environment is one of the most critical issue for drivers and city planners for both environment and efficiency reasons. Traffic lights are one of the main tools used to regulate traffic by diverting the drivers between different paths. Rational drivers, in turn, react to the traffic light duration by evaluating their options and, if necessary, by changing direction in order to reach their destination quicker. In this paper, we introduce a macroscopic traffic model for urban intersections that incorporates this rational behavior of the drivers. Then, we exploit it to show that, by providing additional information about the expected red-time duration to the drivers, one can decrease the amount of congestion in the network and the overall length of the queues at the intersections. Additionally, we develop a control policy for the traffic lights that exploits the reaction of the drivers in order to divert them to a different route to further increase the performances. These claims are supported by extensive numerical simulations.
Higgins M, Xu W, Teng F, et al., 2022, Cyber-physical risk assessment for false data injection attacks considering moving target defences Best practice application of respective cyber and physical reinforcement assets to defend against FDI attacks, International Journal of Information Security, Vol: 22, Pages: 579-589, ISSN: 1615-5262
In this paper, we examine the factors that influence the success of false data injection (FDI) attacks in the context of both cyber and physical styles of reinforcement. Existing research considers the FDI attack in the context of the ability to change a measurement in a static system only. However, successful attacks will require first intrusion into a system followed by construction of an attack vector that can bypass bad data detection to cause a consequence (such as overloading). Furthermore, the recent development of moving target defences (MTD) introduces dynamically changing system topology, which is beyond the capability of existing research to assess. In this way, we develop a full service framework for FDI risk assessment. The framework considers both the costs of system intrusion via a weighted graph assessment in combination with a physical, line overload-based vulnerability assessment under the existence of MTD. We present our simulations on a IEEE 14-bus system with an overlain RTU network to model the true risk of intrusion. The cyber model considers multiple methods of entry for the FDI attack including meter intrusion, RTU intrusion and combined style attacks. Post-intrusion, our physical reinforcement model analyses the required level of topology divergence to protect against a branch overload from an optimised attack vector. The combined cyber and physical index is used to represent the system vulnerability against FDIA.
Ghosh A, Parisini T, 2022, Traffic control in a mixed autonomy scenario at urban intersections: an optimal control approach, IEEE Transactions on Intelligent Transportation Systems, Vol: 23, Pages: 17325-17341, ISSN: 1524-9050
We consider an intersection zone where autonomous vehicles (AVs) and human-driven vehicles (HDVs) can be simulteneously present. As a new vehicle arrives, the traffic controller needs to decide and suggest an optimal sequence of the vehicles which will exit the intersection zone. The traffic controller can inform the time at which an AV can cross the intersection; however, the traffic controller can not communicate with the HDVs, rather the HDVs can only be controlled using the traffic lights. We formulate the problem as an integer constrained nonlinear optimization problem. Since the number of possible combinations increases exponentially with the number of vehicles in the traffic system, we relax the original problem and proposes an algorithm which gives the optimal solution of the relaxed problem and yet only scales linearly with the number of vehicles in the system. The numerical validation shows that our algorithm outperforms the First-In-First-Out (FIFO) algorithm.
Yang G, Rezaee H, Serrani A, et al., 2022, Sensor fault-tolerant state estimation by networks of distributed observers, IEEE Transactions on Automatic Control, Vol: 67, Pages: 5348-5360, ISSN: 0018-9286
We propose a state estimation methodology using a network of distributed observers. We consider a scenario in which the local measurement at each node may not guarantee the system’s observability. In contrast, the ensemble of all the measurements does ensure that the observability property holds. As a result, we design a network of observers such that the estimated state vector computed by each observer converges to the system’s state vector by using the local measurement and the communicated estimates of a subset of observers in its neighborhood. The proposed estimation scheme exploits sensor redundancy to provide robustness against faults in the sensors. Under suitable conditions on the redundant sensors, we show that it is possible to mitigate the effects of a class of sensor faults on the state estimation. Simulation trials demonstrate the effectiveness of the proposed distributed estimation scheme.
Lam HS, Li P, Chen B, et al., 2022, Exponential modulation integral observer for online detection of the fundamental and harmonics in grid-connected power electronics equipment, IEEE Transactions on Control Systems Technology, Vol: 30, Pages: 1821-1833, ISSN: 1063-6536
Harmonic current estimation is required in active power filters for compensation purposes. The most efficient way of calculating the total harmonic current up to the infinite order is to subtract the fundamental component from the distorted current. Direct determination of the fundamental component of a distorted current of mains frequency was realized recently with a third-order modulation integral observer. This article shows that using an exponential modulation function (Exp-MF) for the integral observer will: 1) significantly enhance the robustness of the observer against noise and 2) automatically remove the low-frequency envelope arising from the D/A and A/C sampling processes compared to previous polynomial modulation function. These new and advantageous features are supported with detailed analysis and experimental verification. The robust observer can be implemented in grid-connected power electronics circuits that require the instantaneous information of the fundamental and/or harmonic currents. Practical comparative tests with the adaptive notch filter and recursive discrete Fourier transform (DFT) methods in an active power filter have confirmed the good performance under both the steady and dynamic states of the proposed Exp-MF integral observer.
Zhang K, Keliris C, Polycarpou MM, et al., 2022, Detecting stealthy integrity attacks in a class of nonlinear cyber-physical systems: A backward-in-time approach, Automatica, Vol: 141, ISSN: 0005-1098
This paper proposes a stealthy integrity attack detection methodology for a class of nonlinear cyber–physical systems subject to disturbances. An equivalent increment of the system at a time prior to the attack occurrence time is introduced, which is theoretically proved to be effective to detect stealthy integrity attacks. A backward-in-time estimator is developed via the fixed-point smoother design tool to exploit this equivalent increment and allow the detection of the attack. More specifically, an asymptotically stable incremental system is introduced to characterize stealthy integrity attacks, and its backward-in-time solution at a fixed time prior to the attack occurrence formulates the equivalent increment. When running reversely in time, the divergence property of such an asymptotically stable incremental system enables the equivalent increment to detect stealthy integrity attacks. A fixed-point smoother is introduced to estimate the unknown equivalent increment for a class of Lipschitz nonlinear physical plants, such that the estimation error satisfies the H ∞ performance objective. Based on the equivalent increment and its estimation provided by the smoother, suitable residual and threshold signals are generated, allowing the detection of the considered stealthy integrity attacks. A detectability analysis is conducted to rigorously characterize the class of detectable attacks. Finally, a case study is presented to illustrate the effectiveness of the developed backward-in-time attack detection methodology.
Parisini T, 2022, 2021: The IEEE Control Systems Society…Under Review [President’s Message], IEEE Control Systems, Vol: 42, Pages: 9-11, ISSN: 1066-033X
Chen J, Gallo AJ, Yan S, et al., 2022, Cyber-attack detection and countermeasure for distributed electric springs for smart grid applications, IEEE Access, Vol: 10, Pages: 13182-13192, ISSN: 2169-3536
With increasing installations of grid-connected power electronic converters in the distribution network, there is a new trend of using distributed control in a cyber layer to coordinate the operations of these power converters for improving power system stability. However, cyber-attacks remain a threat to such distributed control. This paper addresses the cyber-attack detection and a countermeasure of distributed electric springs (ESs) that have emerged as a fast demand-response technology. A fully distributed model-based architecture for cyber-attack detection in the communication network is developed. Based on a dynamic model of ES with consensus control, a local state estimator is proposed and practically implemented to monitor the system. The estimator is fully distributed because only local and neighboring information is necessary. A countermeasure for the distributed ESs to ride through the cyber-attack and maintain regulatory services in a microgrid is demonstrated successfully. Experimental results are provided to verify the effectiveness of the proposed cyber-attack detection method and its ride-through capability.
Zhang K, Keliris C, Parisini T, et al., 2022, Identification of sensor replay attacks and physical faults for cyber-physical systems, IEEE Control Systems Letters, Vol: 6, Pages: 1178-1183, ISSN: 2475-1456
This letter proposes a threat discrimination methodology for distinguishing between sensor replay attacks and sensor bias faults, based on the specially designed watermark integrated with adaptive estimation. For each threat type, a watermark is designed based on the changes that the threat imposes on the system. Threat discrimination conditions are rigorously investigated to characterize quantitatively the class of attacks and faults that can be discriminated by the proposed scheme. A simulation is presented to illustrate the effectiveness of our approach.
Zhang K, Kasis A, Polycarpou MM, et al., 2022, A Sensor Watermarking Design for Threat Discrimination, 11th International-Federation-of-Automatic-Control (IFAC) Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Publisher: ELSEVIER, Pages: 433-438, ISSN: 2405-8963
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Barboni A, Yang G, Rezaee H, et al., 2022, On Joint Unknown Input and Sliding Mode Estimation, European Control Conference (ECC), Publisher: IEEE, Pages: 969-974
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Barboni A, Al-Dabbagh AW, Parisini T, 2022, An Event-Triggered Watermarking Strategy for Detection of Replay Attacks, 11th International-Federation-of-Automatic-Control (IFAC) Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Publisher: ELSEVIER, Pages: 317-322, ISSN: 2405-8963
Bin M, Notarnicola I, Parisini T, 2022, Stability, Linear Convergence, and Robustness of the Wang-Elia Algorithm for Distributed Consensus Optimization, IEEE 61st Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 1610-1615, ISSN: 0743-1546
Scandella M, Bin M, Parisini T, 2022, Group-Based Dimensionality Reduction and Estimation for Heterogeneous Large-Scale Traffic Networks, European Control Conference (ECC), Publisher: IEEE, Pages: 1937-1943
Zhang K, Keliris C, Polycarpou MM, et al., 2021, Discrimination between replay attacks and sensor faults for cyber-physical systems via event-triggered communication, European Journal of Control, Vol: 62, Pages: 47-56, ISSN: 0947-3580
In this paper, a threat discrimination methodology is proposed for cyber-physical systems with event-triggered data communication, aiming to identify sensor bias faults from two possible types of threats: replay attacks and sensor bias faults. Event-triggered adaptive estimation and backward-in-time signal processing are the main techniques used. Specifically, distinct incremental systems of the event-triggered cyber-physical system resulting from the considered threat types are established for each threat type, and the difference between their inputs are found and utilized to discriminate the threats. An event-triggered adaptive estimator is then designed by using the event-triggered sampled data based on the system in the attack case, allowing to reconstruct the unknown increments in both the threat cases. The backward-in-time model of the incremental system in the replay attack case is proposed as the signal processor to process the reconstructions of the increments. Such a model can utilize the aforementioned input difference between the incremental systems such that its output has distinct quantitative properties in the attack case and in the fault case. The fault discrimination condition is rigorously investigated and characterizes quantitatively the class of distinguishable sensor bias faults. Finally, a numerical simulation is presented to illustrate the effectiveness of the proposed methodology.
Parisini T, 2021, Open Science and Open Access: An Opportunity Not to Be Missed, IEEE CONTROL SYSTEMS MAGAZINE, Vol: 41, Pages: 10-12, ISSN: 1066-033X
Parisini T, Bin M, 2021, A distributed methodology for approximate uniform global minimum sharing, Automatica, Vol: 131, ISSN: 0005-1098
The paper deals with the distributed minimum sharing problem: a set of decision-makers compute the minimum of some local quantities of interest in a distributed and decentralized way by exchanging information through a communication network. We propose an adjustable approximate solution which enjoys several properties of crucial importance in applications. In particular, the proposed solution has good decentralization properties and it is scalable in that the number of local variables does not grow with the size or topology of the communication network. Moreover, a global and uniform (both in the initial time and in the initial conditions) asymptotic stability result is provided towards a steady state which can be made arbitrarily close to the sought minimum. Exact asymptotic convergence can be recovered at the price of losing uniformity with respect to the initial time.
Invernizzi D, Panza S, Giurato M, et al., 2021, Integration of experimental activities into remote teaching using a quadrotor test-bed, IFAC Workshop on Aerospace Control Education (WACE), Publisher: ELSEVIER, Pages: 49-54, ISSN: 2405-8963
Bin M, Crisostomi E, Ferraro P, et al., 2021, Hysteresis-based supervisory control with application to non-pharmaceutical containment of COVID-19., Annual Reviews in Control, Vol: 52, Pages: 508-522, ISSN: 1367-5788
The recent COVID-19 outbreak has motivated an extensive development of non-pharmaceutical intervention policies for epidemics containment. While a total lockdown is a viable solution, interesting policies are those allowing some degree of normal functioning of the society, as this allows a continued, albeit reduced, economic activity and lessens the many societal problems associated with a prolonged lockdown. Recent studies have provided evidence that fast periodic alternation of lockdown and normal-functioning days may effectively lead to a good trade-off between outbreak abatement and economic activity. Nevertheless, the correct number of normal days to allocate within each period in such a way to guarantee the desired trade-off is a highly uncertain quantity that cannot be fixed a priori and that must rather be adapted online from measured data. This adaptation task, in turn, is still a largely open problem, and it is the subject of this work. In particular, we study a class of solutions based on hysteresis logic. First, in a rather general setting, we provide general convergence and performance guarantees on the evolution of the decision variable. Then, in a more specific context relevant for epidemic control, we derive a set of results characterizing robustness with respect to uncertainty and giving insight about how a priori knowledge about the controlled process may be used for fine-tuning the control parameters. Finally, we validate the results through numerical simulations tailored on the COVID-19 outbreak.
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