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The paper considers the problem of designing Adaptive Feedforward Control (AFC) systems for uncertain SISO linear systems perturbed by multi-sinusoidal disturbances of known frequencies. The proposed approach removes the longstanding assumption that either the sign of the real part or the imaginary part of the transfer function of a stable plant at the frequency of excitation be known for AFC to be applicable, which is referred to in this paper as an SPR-like condition. Notable features of the solution are that persistence of excitation is not required and stability analysis tools based on averaging are avoided; hence, the requirement of an exponentially stable equilibrium for the closed-loop system is circumvented.

We consider the problem of rejection of a sinusoidal disturbance of unknown frequency, phase and magnitude acting on an uncertain internally stable SISO linear system. We present a solution that extends our previous work on adaptive feedforward control of uncertain systems by disposing of the need to know the frequency of the harmonic disturbance. The proposed methodology reposes upon a switching-based combination of an adaptive feedforward control algorithm and a deadbeat frequency estimator. The method accounts for the presence of bounded sensor noise as well as imprecise frequency estimation; it is shown that the regulation error is bounded by a function of the norm of the noise that depends on the choice of the controller and the estimator gains.

Traffic waves, also known as “phantom jams”, are caused by the instability of the system describing the dynamics of traffic flow on highways: slight disturbances in the distribution of vehicles on highways are amplified when the density of the traffic is higher than a certain critical value and eventually generate traffic waves and “stop-and-go” phenomena. We propose a solution to the traffic wave damping problem via shared-control on vehicles and show that the effectiveness of the proposed controller does not depend on human drivers' actions. In other terms, with the developed shared-controller the amplitude of the traffic wave is reduced regardless of the action of the drivers. Simulation results on a traffic control benchmark demonstrating the effectiveness of the controller are also provided.

The problem of covert attacks detection in a network of interconnected subsystems is addressed in this paper. Existing approaches in the area of covert attacks detection have been devoted to centralized systems and are mainly based on mismatch between the attacker's model and the actual plant. Instead, in this paper, we consider a large-scale system where the attacker has full knowledge on the subsystems models. By using the information received from neighboring subsystems and by exploiting the mismatch between a distributed Luenberger observer and a decentralized unknown input observer, we propose a local detection strategy allowing each subsystem to detect anomalies in its neighborhood. The effectiveness of the proposed strategy is shown in a numerical example.

The paper proposes a methodology to effectively address the increasingly important problem of distributed faulttolerant control for large-scale interconnected systems. The approach dealt with combines, in a holistic way, a distributed fault detection and isolation algorithm with a specific tube-based model predictive control scheme. A distributed fault-tolerant control strategy is illustrated to guarantee overall stability and constraint satisfaction even after the occurrence of a fault. In particular, each subsystem is controlled and monitored by a local unit. The fault diagnosis component consists of a passive set-based fault detection algorithm and an active fault isolation one, yielding fault-isolability subject to local input and state constraints. The distributed active fault isolation module - thanks to a modification of the local inputs - allows to isolate the fault that has occurred avoiding the usual drawback of controllers that possibly hide the effect of the faults. The Active Fault Isolation method is used as a decision support tool for the fault tolerant control strategy after fault detection. The distributed design of the tube-based model predictive control allows the possible disconnection of faulty subsystems or the reconfiguration of local controllers after fault isolation. Simulation results on a well-known power network benchmark show the effectiveness of the proposed methodology.

This paper focuses on developing a distributed leader-following fault-tolerant tracking control scheme for a class of high-order nonlinear uncertain multiagent systems. Neural network-based adaptive learning algorithms are developed to learn unknown fault functions, guaranteeing the system stability and cooperative tracking even in the presence of multiple simultaneous process and actuator faults in the distributed agents. The time-varying leader's command is only communicated to a small portion of follower agents through directed links, and each follower agent exchanges local measurement information only with its neighbors through a bidirectional but asymmetric topology. Adaptive fault-tolerant algorithms are developed for two cases, i.e., with full-state measurement and with only limited output measurement, respectively. Under certain assumptions, the closed-loop stability and asymptotic leader-follower tracking properties are rigorously established.

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.

Quadratic boundedness is a notion of stability that is adopted to investigate the design of observers for dynamic systems subject to bounded disturbances. We will show how to exploit such observers for the purpose of fault detection. Toward this end, first of all we present the naive application of quadratic boundedness to construct state observers for linear time-invariant systems with state augmentation, i.e., where additional variables may be introduced to account for the occurrence of a fault. Then a Luenberger observer is designed to estimate the augmented state variable of the system in such a way to detect the fault by using a convenient threshold selection. Finally, such an approach is extended to piecewise affine systems by presenting a hybrid Luenberger observer and its related design based on quadratic boundedness. The design of all the observers for both linear time-invariant and piecewise affine systems can be done by using linear matrix inequalities. Simulation results are provided to show the effectiveness of the proposed approach.

In this paper, a fast-convergent fault detection and isolation architecture is proposed for linear MIMO continuous-time systems. By exploiting a system decomposition technique and making use of kernel-based deadbeat estimators, the state variables can be estimated in a non-asymptotic way. Estimation residuals are then defined to detect the occurrence of a fault and identify the occurring fault function after fault detection. In the noisy scenario, thresholds are defined for the residual to distinguish the effect of the noise from that of the fault. Numerical examples are included to characterize the effectiveness of the proposed FDI architecture.

Indoor Air Quality monitoring is an essentialingredient of intelligent buildings. The release of variousairborne contaminants into the buildings, compromises thehealth and safety of occupants. Therefore, early contaminantdetection is of paramount importance for the timely activationof proper contingency plans in order to minimize the impact ofcontaminants on occupants health. The objective of this workis to enhance the performance of a distributed contaminantdetection methodology, in terms of the minimum detectablecontaminant release rates, by considering the joint problemof partitioning selection and observer gain design. Towardsthis direction, a detectability analysis is performed to deriveappropriate conditions for the minimum guaranteed detectablecontaminant release rate for specific partitioning configurationand observer gains.The derived detectability conditions arethen exploited to formulate and solve an optimization problemfor jointly selecting the partitioning configuration and observergains that yield the best contaminant detection performance.

This paper presents a novel observer-based method for estimating the parameters of an exponentially damped sinusoid, including the frequency, amplitude, phase and the damping factor. A second-order sliding-mode-based adaptation law is designed for estimating the frequency and the damping factor, while amplitude and phase are obtainable from the said parameters by straightforward algebra. The stability and robustness analysis in presence of an additive disturbance proves the existence of a tuning parameter set for which the estimator’s dynamics are asymptotic stable and the estimation error asymptotically converges to a residual set whose size depends on the amplitude of the disturbance. Numerical and comparative examples show the effectiveness of the proposed estimation approach.

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.

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.

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.

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 modelling error is characterised 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.

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.

The simultaneous estimation of parameters and states in continuous-time linear MISO systems is considered in this paper. The estimates of system's parameters and states are provided simultaneously by a single estimator, where Volterra operators are suitably applied to the I/O measurements. This makes the estimator independent on the initial conditions thanks to the design of suitably shaped kernel functions equipped with non-asymptotic properties. As a result, in the noise-free scenario, instantaneous convergence can be obtained. Nohigh-gain injection nor periodic resetting is necessary. Numerical examples are reported showing the effectiveness of the proposed estimator.

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.

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.

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.

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 one mains cycle.

This paper presents an adaptive sensor fault diagnosis and accommodation scheme for multiple sensor bias faults for a class of input-output nonlinear systems subject to modeling uncertainty and measurement noise. The proposed scheme consists of a nonlinear estimation model that includes an adaptive component which is initiated upon the detection of a fault, in order to approximate the magnitude of the bias faults. A detectability condition characterizing the class of detectable sensor bias faults is derived and the robustness and stability properties of the adaptive scheme are presented. The estimation of the magnitude of the sensor bias faults allows the identification of the faulty sensors and it is also used for fault accommodation purposes. The effectiveness of the proposed scheme is demonstrated through a simulation example. 6334 6339

This paper proposes a rigorous anomaly detection scheme, developed to spot power system operational changes which are inconsistent with the models used by operators. This novel technique relies on a state observer, with guaranteed estimation error convergence, suitable to be implemented in real time, and it has been developed to fully address this important issue in power systems. The proposed method is fitted to the highly nonlinear characteristics of the network, with the states of the nonlinear generator model being estimated by means of a linear time-varying estimation scheme. Given the reliance of the existing dynamic security assessment tools in industry on nominal power system models, the suggested methodology addresses cases when there is deviation from assumed system dynamics, enhancing operators' awareness of system operation. It is based on a decision scheme relying on analytical computation of thresholds, not involving empirical criteria which are likely to introduce inaccurate outcomes. Since false-alarms are guaranteed to be absent, the proposed technique turns out to be very useful for system monitoring and control. The effectiveness of the anomaly detection algorithm is shown through detailed realistic case studies in two power system models.