arXiv subjects

Khashayar Khorasani

Publications and source records attributed to Khashayar Khorasani.

17 recordsLinked to original sources

arXiv2026

Resilient Output Containment under Undisclosed Leader Dynamics and Actuator Attacks

This work studies resilient output containment for heterogeneous linear multi-agent systems with actuator cyber-attacks over directed network topologies. The leaders generate bounded locally absolutely continuous trajectories; however, their dynamics, velocity bounds, and motion envelopes are undisclosed to the followers. The cyber-attack model includes state- and input-correlated, as well as bounded exogenous actuator false-data terms. A continuous two-layer adaptive control architecture is proposed. The first layer is a virtual-actuator reconfiguration layer that uses partial state measurements to compensate for actuator attacks in the local tracking-error dynamics. The second layer is a network interface that generates task-space commands via an adaptive interaction protocol. This protocol uses only neighbor-exchanged network-interface states whose dimensions match those of the plant output, and it does not require global graph knowledge for parameter tuning. For directed graphs, under a leader-rooted united spanning-tree condition, a nonsmooth Lyapunov analysis yields asymptotic containment at the command level. The physical outputs then converge to the leader convex hull up to a residual determined by the command-tracking local controllers. Simulation results using a network of quadrotors with damped suspended loads illustrate the performance of attack recovery and containment tracking.

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arXiv2025

A Dynamic Coding Scheme to Prevent Covert Cyber-Attacks in Cyber-Physical Systems

In this paper, we address two main problems in the context of covert cyber-attacks in cyber-physical systems (CPS). First, we aim to investigate and develop necessary and sufficient conditions in terms of disruption resources of the CPS that enable adversaries to execute covert cyber-attacks. These conditions can be utilized to identify the input and output communication channels that are needed by adversaries to execute these attacks. Second, this paper introduces and develops a dynamic coding scheme as a countermeasure against covert cyber-attacks. Under certain conditions and assuming the existence of one secure input and two secure output communication channels, the proposed dynamic coding scheme prevents adversaries from executing covert cyber-attacks. A numerical case study of a flight control system is provided to demonstrate the capabilities of our proposed and developed dynamic coding scheme.

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arXiv2025

Enhancing Fault Detection and Isolation in an All-Electric Auxiliary Power Unit (APU) Gas Generator by Utilizing Starter/Generator Signal

This study proposes a novel paradigm for enhancing fault detection and isolation (FDI) of gas generators in all-electric auxiliary power unit (APU) by utilizing shaft power information from the starter/generator. First, we conduct a pioneering investigation into the challenges and opportunities for FDI brought about by APU electrification. Our analysis reveals that the electrification of APU opens up new possibilities for utilizing shaft power estimates from starter/generator to improve gas generator FDI. We then provide comprehensive theoretical and analytical evidence demonstrating why, how, and to what extent, the shaft power information from the starter/generator can fundamentally enhance the estimation accuracy of system states and health parameters of the gas generator, while also identifying the key factors influencing these improvements in FDI performance. The effectiveness of the proposed paradigm and its theoretical foundations are validated through extensive Monte Carlo simulations. Furthermore, through comprehensive comparative analysis with state-of-the-art gas generator fault diagnosis methods, our experimental results not only demonstrate the superior performance of the proposed approach but also validate that the diagnostic capabilities of existing advanced FDI techniques can be substantially enhanced by incorporating shaft power information. And the observed performance improvement patterns strongly align with our theoretical analysis, verifying both the effectiveness and guiding significance of our theoretical framework. These research findings provide a unique perspective in answering three fundamental questions: why joint fault diagnosis of the starter/generator and gas generator is essential, how it can be implemented, and what factors determine its effectiveness, thereby opening up promising new avenues for FDI technologies in all-electric APU systems.

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arXiv2025

Zero Dynamics Attack Detection and Isolation in Cyber-Physical Systems with Event-triggered Communication

This paper investigates the problem of Zero Dynamics (ZD) cyber-attack detection and isolation in Cyber-Physical Systems (CPS). By utilizing the notion of auxiliary systems with event-based communications, we will develop a detection mechanism capable of detecting and isolating the ZD cyber-attack even when the attackers have full knowledge of the dynamics of the auxiliary system and can launch False Data Injection (FDI) attacks on all the communication channels. More specifically, we will utilize a self-triggering rule for the communication channels connecting the auxiliary system with the Command & Control (C&C) center, leveraging its properties to detect the ZD cyber-attack. Finally, the effectiveness and capabilities of our approach are verified and demonstrated through simulation case studies.

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arXiv2021

Fault Diagnosis of Nonlinear Systems Using a Hybrid-Degree Dual Cubature-based Estimation Scheme

In this paper, a novel hybrid-degree dual estimation approach based on cubature rules and cubature-based nonlinear filters is proposed for fault diagnosis of nonlinear systems through simultaneous state and time-varying parameter estimation. Our proposed dual nonlinear filtering scheme is developed based on case-dependent cubature rules that are motivated by the following observations and facts, namely (i) dynamic characteristics of nonlinear system states and parameters generally are distinct and posses different degrees of complexities, and (ii) performance of cubature rules depend on the system dynamics and vary due to handling of high-dimensional integrations approximations. For improving the robustness capability of our proposed methodologies modified cubature point propagation method is incorporated. The performance of our proposed dual estimation strategy is demonstrated and evaluated by application to a nonlinear gas turbine engine for addressing the component fault diagnosis problem within an integrated fault detection, isolation and identification framework. Robustness analysis is implemented to verify the capability of our proposed approaches to deal with parametric uncertainties and unmodeled dynamics. Extensive simulation case studies and discussions with respect to component fouling, erosion or abrupt faults are provided to substantiate and justify the superiority of our proposed fault diagnosis methodology when compared to other well-known alternative diagnostic techniques such as the Unscented Kalman Filters (UKF) and Particle Filters (PF) that are commonly available in the literature.

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arXiv2020

Undetectable Cyber Attacks on Communication Links in Multi-Agent Cyber-Physical Systems

The objective in this paper is to study and develop conditions for a network of multi-agent cyber-physical systems (MAS) where a malicious adversary can utilize vulnerabilities in order to ensure and maintain cyber attacks undetectable. We classify these cyber attacks as undetectable in the sense that their impact cannot be observed in the generated residuals. It is shown if an agent that is the root of a rooted spanning tree in the MAS graph is under a cyber attack, the attack is undetectable by the entire network. Next we investigate if a non-root agent is compromised, then under certain conditions cyber attacks can become detectable. Moreover, a novel cyber attack that is designated as quasi-covert cyber attack is introduced that can be used to eliminate detectable impacts of cyber attacks to the entire network and maintain these attacks as undetected. Finally, an event-triggered based detector is proposed that can be used to detect the quasi-covert cyber attacks. Numerical simulations are provided to illustrate the effectiveness and capabilities of our proposed methodologies.

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arXiv2020

Mitigation and Resiliency of Multi-Agent Systems Subject to Malicious Cyber Attacks on Communication Links

This paper aims at investigating a novel type of cyber attack that is injected to multi-agent systems (MAS) having an underlying directed graph. The cyber attack, which is designated as the controllability attack, is injected by the malicious adversary into the communication links among the agents. The adversary, leveraging the compromised communication links disguises the cyber attack signals and attempts to take control over the entire network of MAS. The adversary aims at achieving this by directly attacking only a subset of the multi-agents. Conditions under which the malicious hacker has control over the entire MAS network are provided. Two notions of security controllability indices are proposed and developed. These notions are utilized as metrics to evaluate the controllability that each agent provides to the adversary for executing the malicious cyber attack. Furthermore, the possibility of introducing zero dynamics cyber attacks on the MAS through compromising the communication links is also investigated. Finally, an illustrative numerical example is provided to demonstrate the effectiveness of our proposed methods.

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arXiv2020

Cyber Attack and Machine Induced Fault Detection and Isolation Methodologies for Cyber-Physical Systems

In this paper, the problem of simultaneous cyber attack and fault detection and isolation (CAFDI) in cyber-physical systems (CPS) is studied. The proposed solution methodology consists of two filters on the plant and the command and control (C\&C) sides of the CPS and an unknown input observer (UIO) based detector on the plant side. Conditions under which the proposed methodology can detect deception attacks, such as covert attacks, zero dynamics attacks, and replay attacks are characterized. An advantage of the proposed methodology is that one does not require a fully secured communication link which implies that the communication link can be compromised by the adversary while it is used to transmit the C\&C side observer estimates. Also, it is assumed that adversaries have access to parameters of the system, filters, and the UIO-based detector, however, they do not have access to all the communication link channels. Conditions under which, using the communication link cyber attacks, the adversary cannot eliminate the impact of actuator and sensor cyber attacks are investigated. To illustrate the capabilities and effectiveness of the proposed CAFDI methodologies, simulation case studies are provided and comparisons with detection methods that are available in the literature are included to demonstrate the advantages and benefits of our proposed solutions.

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arXiv2019

Unbiased Inversion-Based Fault Estimation of Systems with Non-Minimum Phase Fault-to-Output Dynamics

We propose a framework for inversion-based estimation of certain categories of faults in discrete-time linear systems. The fault signal, as an unknown input, is reconstructed from its projections onto two subspaces. One projection is achieved through an algebraic operation, whereas the other is given by a dynamic filter whose poles coincide with the transmission zeros of the system. A feedback is then introduced to stabilize the above filter as well as to provide an unbiased estimate of the unknown input. Our solution has two distinctive and practical advantages. First, it represents a unified approach to the problem of inversion of both minimum and non-minimum phase systems as well as systems having transmission zeros on the unit circle. Second, the feedback structure makes the proposed scheme robust to noise. We have shown that the proposed inversion filter is unbiased for certain categories of faults. Finally, we have illustrated the performance of our proposed methodologies through numerous simulation studies.

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arXiv2017

Ensemble Kalman Filters (EnKF) for State Estimation and Prediction of Two-time Scale Nonlinear Systems with Application to Gas Turbine Engines

In this paper, we propose and develop a methodology for nonlinear systems health monitoring by modeling the damage and degradation mechanism dynamics as "slow" states that are augmented with the system "fast" dynamical states. This augmentation results in a two-time scale nonlinear system that is utilized for development of health estimation and prediction modules within a health monitoring framework. Towards this end, a two-time scale filtering approach is developed based on the ensemble Kalman filtering (EnKF) approach by taking advantage of the model reduction concept. The performance of our proposed two-time scale ensemble Kalman filters is shown to be superior and less computationally intensive in terms of the equivalent flop (EF) complexity metric when compared to well-known particle filtering (PF) approaches. Our proposed methodology is then applied to a gas turbine engine that is affected by erosion of the turbine as the degradation phenomenon and damage mechanism. Extensive comparative studies are conducted to validate and demonstrate the advantages and capabilities of our proposed framework and methodology.

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arXiv2017

Inversion-Based Output Tracking and Unknown Input Reconstruction of Square Discrete-Time Linear Systems

In this paper, we propose a framework for output tracking control of both minimum phase (MP) and non-minimum phase (NMP) systems {as well as systems with transmission zeros on the unit circle}. Towards this end, we first address the problem of unknown state and input reconstruction of non-minimum phase systems. An unknown input observer (UIO) is designed that accurately reconstructs the minimum phase states of the system. The reconstructed minimum phase states serve as inputs to an FIR filter for a delayed non-minimum phase state reconstruction. It is shown that a quantified upper bound of the reconstruction error exponentially decreases as the estimation delay is increased. Therefore, an almost perfect reconstruction can be achieved by selecting the delay to be sufficiently large. Our proposed inversion scheme is then applied to solve the output-tracking control problem. {We have also proposed a methodology to handle the output tracking problem of systems that have transmission zeros on the unit circle in addition to MP and NMP zeros.} Simulation case studies are also presented that demonstrate the merits and capabilities of our proposed methodologies.

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arXiv2016

A Data-driven Approach to Actuator and Sensor Fault Detection, Isolation and Estimation in Discrete-Time Linear Systems

In this work, we propose explicit state-space based fault detection, isolation and estimation filters that are data-driven and are directly identified and constructed from only the system input-output (I/O) measurements and through estimating the system Markov parameters. The proposed methodology does not involve a reduction step and does not require identification of the system extended observability matrix or its left null space. The performance of our proposed filters is directly connected to and linearly dependent on the errors in the Markov parameters identification process. The estimation filters operate with a subset of the system I/O data that is selected by the designer. It is shown that the proposed filters provide asymptotically unbiased estimates by invoking low order filters as long as the selected subsystem has a stable inverse. We have derived the estimation error dynamics in terms of the Markov parameters identification errors and have shown that they can be directly synthesized from the healthy system I/O data. Consequently, the estimation errors can be effectively compensated for. Finally, we have provided several illustrative case study simulations that demonstrate and confirm the merits of our proposed schemes as compared to methodologies that are available in the literature.

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arXiv2016

A Geometric Approach to Fault Detection and Isolation of Two-Dimensional (2D) Systems

In this work, we develop a novel fault detection and isolation (FDI) scheme for discrete-time two-dimensional (2D) systems that are represented by the Fornasini-Marchesini model II (FMII). This is accomplished by generalizing the basic invariant subspaces including unobservable, conditioned invariant and unobservability subspaces of 1D systems to 2D models. These extensions have been achieved and facilitated by representing a 2D model as an infinite dimensional (Inf-D) system on a Banach vector space, and by particularly constructing algorithms that compute these subspaces in a \emph{finite and known} number of steps. By utilizing the introduced subspaces the FDI problem is formulated and necessary and sufficient conditions for its solvability are provided. Sufficient conditions for solvability of the FDI problem for 2D systems using both deadbeat and LMI filters are also developed. Moreover, the capabilities and advantages of our proposed approach are demonstrated by performing an analytical comparison with the currently available 2D geometric methods in the literature. Finally, numerical simulations corresponding to an approximation of a hyperbolic partial differential equation (PDE) system of a heat exchanger, that is mathematically represented as a 2D model, have also been provided.

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arXiv2016

Invariant Subspaces of Riesz Spectral Systems with Application to Fault Detection and Isolation

A large class of hyperbolic and parabolic partial differential equation (PDE) systems, such as reaction-diffusion processes, when expressed in the infinite-dimensional (Inf-D) framework can be represented as Riesz spectral (RS) systems. Compared to the finite dimensional (Fin-D) systems, the geometric theory of Inf-D systems for addressing certain fundamental control problems, such as disturbance decoupling and fault detection and isolation (FDI), is rather quite limited due to complexity and existence of various types of invariant subspaces notions. Interestingly enough, these invariant concepts are equivalent for Fin-D systems, although they are different in Inf-D representation. In this work, first equivalence of various types of invariant subspaces that are defined for RS systems are investigated. This enables one to define and specify the unobservability subspace for RS systems. Specifically, necessary and sufficient conditions are derived for equivalence of various types of conditioned invariant subspaces. Moreover, by using duality properties, various controlled invariant subspaces are developed. It is then shown that finite-rankness of the output operator enables one to derive algorithms for computing invariant subspaces that under certain conditions, and unlike methods in the literature, converge in a finite number of steps. A geometric FDI methodology for RS systems is then developed by invoking the introduced invariant subspaces. Finally, necessary and sufficient conditions for solvability of the FDI problem are provided and analyzed

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arXiv2016

Particle Filter-Based Fault Diagnosis of Nonlinear Systems Using a Dual Particle Filter Scheme

In this paper, a dual estimation methodology is developed for both time-varying parameters and states of a nonlinear stochastic system based on the Particle Filtering (PF) scheme. Our developed methodology is based on a concurrent implementation of state and parameter estimation filters as opposed to using a single filter for simultaneously estimating the augmented states and parameters. The convergence and stability of our proposed dual estimation strategy are shown formally to be guaranteed under certain conditions. The ability of our developed dual estimation method is testified to handle simultaneously and efficiently the states and time-varying parameters of a nonlinear system in a context of health monitoring which employs a unified approach to fault detection, isolation and identification is a single algorithm. The performance capabilities of our proposed fault diagnosis methodology is demonstrated and evaluated by its application to a gas turbine engine through accomplishing state and parameter estimation under simultaneous and concurrent component fault scenarios. Extensive simulation results are provided to substantiate and justify the superiority of our proposed fault diagnosis methodology when compared with another well-known alternative diagnostic technique that is available in the literature.

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arXiv2016

An $H_{\infty}$ Cooperative Fault Recovery Control of Multi-Agent Systems

In this work, an $H_{\infty}$ performance fault recovery control problem for a team of multi-agent systems that is subject to actuator faults is studied. Our main objective is to design a distributed control reconfiguration strategy such that \textbf{a)} in absence of disturbances the state consensus errors either remain bounded or converge to zero asymptotically, \textbf{b)} in presence of actuator fault the output of the faulty system behaves exactly the same as that of the healthy system, and \textbf{c)} the specified $H_{\infty}$ performance bound is guaranteed to be minimized in presence of bounded energy disturbances. The gains of the reconfigured control laws are selected first by employing a geometric approach where a set of controllers guarantees that the output of the faulty agent imitates that of the healthy agent and the consensus achievement objectives are satisfied. Next, the remaining degrees of freedom in the selection of the control law gains are used to minimize the bound on a specified $H_{\infty}$ performance index. The effects of uncertainties and imperfections in the FDI module decision in correctly estimating the fault severity as well as delays in invoking the reconfigured control laws are investigated and a bound on the maximum tolerable estimation uncertainties and time delays are obtained. Our proposed distributed and cooperative control recovery approach is applied to a team of five autonomous underwater vehicles to demonstrate its capabilities and effectiveness in accomplishing the overall team requirements subject to various actuator faults, delays in invoking the recovery control, fault estimation and isolation imperfections and unreliabilities under different control recovery scenarios.

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arXiv2015

Sensor Fault Detection, Isolation and Identification Using Multiple Model-based Hybrid Kalman Filter for Gas Turbine Engines

In this paper, a novel sensor fault detection, isolation and identification (FDII) strategy is proposed by using the multiple model (MM) approach. The scheme is based on multiple hybrid Kalman filters (HKF) which represents an integration of a nonlinear mathematical model of the system with a number of piecewise linear (PWL) models. The proposed fault detection and isolation (FDI) scheme is capable of detecting and isolating sensor faults during the entire operational regime of the system by interpolating the PWL models using a Bayesian approach. Moreover, the proposed multiple HKF-based FDI scheme is extended to identify the magnitude of a sensor fault by using a modified generalized likelihood ratio (GLR) method which relies on the healthy operational mode of the system. To illustrate the capabilities of our proposed FDII methodology, extensive simulation studies are conducted for a nonlinear gas turbine engine. Various single and concurrent sensor fault scenarios are considered to demonstrate the effectiveness of our proposed on-line hierarchical multiple HKF-based FDII scheme under different flight modes. Finally, our proposed HKF-based FDI approach is compared with various filtering methods such as the linear, extended, unscented and cubature Kalman filters (LKF, EKF, UKF and CKF, respectively) corresponding to both interacting and non-interacting multiple model (MM) based schemes. Our comparative studies confirm the superiority of our proposed HKF method in terms of promptness of the fault detection, lower false alarm rates, as well as robustness with respect to the engine health parameters degradations.

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