Bank Of Unknown Input Observers Research Articles

A robustly stabilizing fault‐tolerant MPC: A performance improvement‐based approach

AbstractIn terms of model predictive control (MPC) performance degradation caused by operational faults, in this article, a robust MPC strategy with active fault tolerance properties is proposed. The proposed strategy incorporates a fault supervision layer into the structure of conventional cost‐contracting formulation‐based robust MPC for the online update of the nominal controller model in the event of faults. The robust MPC is based on multiplant uncertainty, while the supervisory layer consists of a bank of unknown input observers and a decision‐making algorithm. Simulation results in a nonlinear polymerization reactor subject to process faults demonstrate that the proposed approach offers superior performance compared to the conventional strategy.

Defending State-Feedback Based Controllers Against Sensor Attacks

Context: This paper is motivated by the need to improve the resilience of industrial control systems. Many control systems currently operating in the industry were designed and implemented before the boom in communications (wired and wireless networks) within industrial control systems. However, nowadays they operate connected to the communications network. This increase in connectivity has made the systems susceptible to cyber-attacks that seek to degrade the proper operation of the control loop even affecting only one sensor.
 Method: We use concepts from fault tolerant control and classic control theory to show that it is possible to reconstruct the system state without (any) one of the system outputs. This fact is used in the recalculation of the control signal through an algorithm of attack detection and isolation, to avoid for an attack to be feedback to the system, mitigating its effect. We show the effectiveness of our proposal with simulations on a four-tanks testbed using Matlab and Simulink.
 Results: We show that a bank of unknown input observers can be designed to recover true information from attacked sensors, i.e., recover the information without the effect of the attack. Therefore, the estimation obtained from those observers can be utilized for computing a control action that mitigates the effect of the attack.
 Conclusions: This mitigation of the attack prevents a single sensor attack from signi cantly impair the action of low-level controllers, improving the resilience on the system just modifying the digital controller architecture. The development presented is limited to cyber-attacks on system sensors pre- sented one at a time, that still can compromise seriously the system behavior. Future work will address the extension of the presented results in situations with simultaneous attacks on more than one sensor, and/or consider attacks on the control system actuators.

Observer-based distributed fault detection and isolation for multi-agent systems with time delays and disturbances

In this article, the problem of distributed fault detection and isolation for single fault is considered for multi-agent systems affected by disturbances and communication delays. A bank of unknown input observers based on local information are constructed for distributed fault detection and isolation, and the sufficient conditions are presented to guarantee that the fault-detection residuals generated by unknown input observers satisfy [Formula: see text]. The logic of distributed fault detection is as follows: a agent can be declared as faulty when it's fault residuals exceed the threshold, and the fault residuals of its adjacent nodes are less than the threshold, declaring the existence of the fault.

On Joint Reconstruction of State and Input-Output Injection Attacks for Nonlinear Systems

We address the problem of robust state reconstruction for discrete-time nonlinear systems when the actuators and sensors are injected with (potentially unbounded) attack signals. Exploiting redundancy in sensors and actuators and using a bank of unknown input observers (UIOs), we propose an observer-based estimator capable of providing asymptotic estimates of the system state and attack signals under the condition that the numbers of sensors and actuators under attack are sufficiently small. Using the proposed estimator, we provide methods for isolating the compromised actuators and sensors. Numerical examples are provided to demonstrate the effectiveness of our methods.

Model-Based FDI Applied to a Piezoelectric Active Vibration Suppression System for Smart Flexible Spacecraft

Fault Detection and Isolation (FDI) techniques have captured extensive interest and attention in modern autonomous systems; in particular, they are of foremost importance in space applications, due to their scientific relevance, cost and current inability of doing on-orbit maintenance of space systems. In this scenario, FDI strategies are required to counteract possible failure events that, if not properly handled, can reduce system performance or compromise the realization of the mission objectives. In this paper, a model-based FDI strategy is implemented onboard a satellite equipped with a very large mesh reflector on which a distributed network of smart actuators/sensors is mounted to actively counteract undesired elastic vibrations. In particular, the detection and isolation of a possible piezo-actuator failure occurring in the Active Vibration Control (AVC) system of the antenna is addressed by a bank of Unknown Input Observers (UIOs). The design of the proposed UIOs is derived by solving a Linear Matrix Inequality (LMI) problem, which provides the conditions for their existence, and it is based on the linearized 3D state-space model of the controlled spacecraft, under the assumption that all the uncertainties, exogenous disturbances and measurement noises are neglected. Furthermore, pole assignment in the sense of D-stability is integrated in the standard formulation of the UIO to guarantee an adequate transient behaviour of the observers. Finally, an extensive Monte Carlo simulation campaign is conducted to assess the effectiveness of the proposed FDI architecture and its robustness against modelling uncertainties and measurement noise.

A Distributed Cyber-Attack Detection Scheme With Application to DC Microgrids

DC microgrids often present a hierarchical control architecture, requiring integration of communication layers. This leads to the possibility of malicious attackers disrupting the overall system. Motivated by this application, in this article, we present a distributed monitoring scheme to provide attack-detection capabilities for linear large-scale systems. The proposed architecture relies on a Luenberger observer together with a bank of unknown-input observers at each subsystem, providing attack detection capabilities. We describe the architecture and analyze conditions under which attacks are guaranteed to be detected, and, conversely, when they are stealthy . Our analysis shows that some classes of attacks cannot be detected using either module independently; rather, by exploiting both modules simultaneously, we are able to improve the detection properties of the diagnostic tool as a whole. Theoretical results are backed up by simulations, where our method is applied to a realistic model of a low-voltage DC microgrid under attack.

An Unknown Input Multiobserver Approach for Estimation and Control Under Adversarial Attacks

We address the problem of state estimation, attack isolation, and control of discrete-time linear time-invariant systems under (potentially unbounded) actuator and sensor false data injection attacks. Using a bank of unknown input observers, each observer leading to an exponentially stable estimation error (in the attack-free case), we propose an observer-based estimator that provides exponential estimates of the system state despite actuator and sensor attacks. Exploiting sensor and actuator redundancy, the estimation scheme is guaranteed to work if a sufficiently small subset of sensors and actuators is under attack. Using the proposed estimator, we provide tools for reconstructing and isolating actuator and sensor attacks, and a control scheme capable of stabilizing the closed-loop dynamics by switching off isolated actuators. Simulation results are presented to illustrate the performance of our tools.

Fault Detection and Isolation for Complex Thermal Management Systems

This paper presents a fault detection and isolation (FDI) approach for actuator faults of complex thermal management systems. In the case of safety critical systems, early fault diagnosis not only improves system reliability, but can also help prevent complete system failure (i.e., aircraft system). In this work, a robust unknown input observer (UIO)-based actuator FDI approach is applied on an example aircraft fluid thermal management system (FTMS). Robustness is achieved by decoupling the effect of unknown inputs modeled as additive disturbances (i.e., modeling errors, linearization errors, parameter variations, or model order reduction errors) from the residuals generated from a bank of UIOs. Robustness is central to avoid false alarms without reducing residual sensitivity to actual faults in the system. System dynamics are modeled using a graph-based approach. A structure preserving aggregation-based model-order reduction technique is used to reduce the complexity of the dynamic model. A reduced-order linearized state space model is then used in a bank of UIOs to generate a set of structured robust (in the sense of disturbance decoupling) residuals. Simulation and experimental results show successful (i.e., no false alarms) actuator FDI in the presence of unknown inputs.

Unknown input observer design for fault detection and diagnosis in a continuous stirred-tank reactor

Early and accurate fault detection and diagnosis (FDD) minimises downtime, increases the safety and reliability of plant operation, and reduces manufacturing costs. This paper presents a robust FDD strategy for a nonlinear system using a bank of unknown input observers (UIO). The approach is based on structure residual generation that provides not only decoupling of faults from model uncertainties and unknown input disturbance but also decoupling the effect of a fault from the effects of other faults. The generated residual was evaluated through the statistical threshold to avoid fault missing or false alarm. The performance of the robust FDD scheme was assessed by some sensor fault scenarios created in a continuous stirred-tank reactor (CSTR). The simulation result showed the effectiveness of the proposed approach.

Distributed Observes for Cyberattack Detection and Isolation in Formation-Flying Unmanned Aerial Vehicles

In this paper, cyberattack detection and isolation is studied for a network of formation-flying unmanned aerial vehicles. As the unmanned aerial vehicles communicate to reach consensus on their states while making the formation, the communication network among the unmanned aerial vehicles makes them vulnerable to a potential attack from malicious adversaries. Two types of attacks pertinent to a network of unmanned aerial vehicles have been considered: a node attack on the unmanned aerial vehicles and a deception attack on the communication between the unmanned aerial vehicles. Unmanned aerial vehicle formation control involves using a consensus algorithm to maintain a prespecified formation. Two kinds of cyberattacks in the unmanned aerial vehicle network (node and communication path deception) are considered with their respective models in the formation setup. A bank of unknown input observer-based distributed fault detection schemes as well as with a rule based on residual generated using the bank of unknown input observers are proposed to detect the attacks and to identify the compromised unmanned aerial vehicle in the formation. Furthermore, an algorithm is developed to remove the faulty unmanned aerial vehicle from the network and to keep the compromised unmanned aerial vehicle isolated once an attack is detected, while maintaining the flight formation with a missing unmanned aerial vehicle node.

Fault‐Tolerant Control Allocation for Overactuated Nonlinear Systems

AbstractThis paper addresses the problem of fault‐tolerant control allocation for input affine nonlinear systems. The proposed scheme is divided in three main tasks: fault detection and estimation using a nonlinear observer, fault isolation through a bank of unknown input observers with a resetting policy to reduce the effects of nonlinearities and control reconfiguration based on reduced order allocation. Analytical results regarding the isolability and reconfigurability of actuator faults are derived and a simulation example is used to illustrate the the proposed fault tolerant control methodology.

Fault Detection and Isolation Method Based on H−/H∞ Unknown Input Observer Design in Finite Frequency Domain

AbstractThis paper proposes an actuator fault detection and isolation strategy based on a bank of unknown input observers with finite frequency specifications. In order to deal with actuator fault diagnosis problem, a bank of H−/H∞ unknown input observers are designed to generate residuals, which are insensitive to the corresponding faults but sensitive to the other actuators faults, and meanwhile robust against the unknown disturbances. In this paper, the actuator faults and unknown disturbances are considered to belong to finite frequency domains, and two finite frequency performance indices are used to measure the fault sensitivity and the disturbance robustness of the residuals. Furthermore, some parameters for extra design of freedom are introduced in the H−/H∞ unknown input observers design. Based on the generalised Kalman‐Yakubovich‐Popov (GKYP) lemma, the design conditions of the H−/H∞ unknown input observer are derived and formulated as linear matrix inequalities (LMIs). Finally, a VTOL aircraft model is used to demonstrate the performance of the proposed fault diagnosis scheme.

Unknown Input Fault Detection and Isolation Observer Design for Neutral Systems

AbstractThis paper deals with actuator fault diagnosis of neutral delayed systems with multiple time delays using an unknown input observer. The main purpose is to design an observer that guarantees the asymptotic stability of the estimate error dynamics and the actuator fault detection. The existence conditions for such an observer are established. The main problem studied in this paper aims at designing observer‐based fault detection and isolation. The designed observer enhances the robust diagnosis performance, including rapidity and accuracy, and generates residuals that enjoy perfect decoupling properties among faults. Based on Lyapunov stability theory, the design of the observer is formulated in terms of linear matrix inequalities, and the diagnosis scheme is based on a bank of unknown input observers for residual generation that guarantees fault detection and isolation in the presence of external disturbances. A numerical example is presented to illustrate the efficiency of the proposed approach.

Model-based FDIR and Fault Accommodation for a Rendezvous Mission around the Mars Planet: the Mars Sample Return Case

The research work presented in the paper addresses the design of a model-based fault diagnosis and fault recovery system for any faults occurring in the actuator and sensor units of the chaser spacecraft of the ESA Mars Sample Return (MSR) mission. Key features of the proposed method are the use of a parity space and covariance-based strategy with jointly a H∞ observer for fault diagnosis of sensor faults, a H∞/H- filter for robust fault detection of actuator faults and a bank of unknown input observers jointly used with a dot product of vectors strategy for actuator faults. For fault accommodation, a ”retreat” FDIR strategy scheduled by the FDI unit, is retained. The proposed FDIR architecture obeys to a hierarchical one and fits the industrial requirements. Especially, it is compliant with the Aurora avionics architecture. A simulation campaign, based on a nonlinear high-fidelity simulator developed by GMV space and Thales Alenia Space industries, is conducted under highly realistic conditions.

Robust Model-based Fault Diagnosis of Thruster Faults in Spacecraft

The research work presented in this paper addresses the design and validation of a robust model-based fault diagnosis system to detect and isolate any thruster fault occurring in the thruster-based propulsion system of the chaser spacecraft during rendezvous on a circular orbit around the Mars planet. The REFERENCE mission is the ESA Mars Sample Return (MSR) mission. Key features of the proposed method are the use of a Hi/H∞ filter for robust fault detection and a bank of unknown input observers jointly used with a dot product of vectors strategy for fault isolation. Robust performance over a large range of uncertainties (a total of 36 uncertainties is considered) is formally demonstrated through the μ3 formalism.

Fault Detection And Isolation Using Unknown Input Observers With Structured Residual Generation

Detection and isolation of faults are important tasks for improving the reliability of process industries in order to enhance their products quality. This paper investigates the detection and isolation of faults using structured residuals. Actuator and sensor faults are considered. Residuals are generated using a bank of unknown input observers (UIO). Three-tank benchmark system was used as a prototype of many process industries. Simulation results show the effectiveness of the studied method.

Distributed fault detection for interconnected second-order systems

In this paper, the existence of unknown input observers for networks of interconnected second-order linear time invariant systems is studied. Two classes of distributed control systems of large practical relevance are considered. It is proved that for these systems, one can construct a bank of unknown input observers, and use them to detect and isolate faults in the network. The result presents a distributed implementation. In particular, by exploiting the system structure, this work provides further insight into the design of UIO for networked systems. Moreover, the importance of certain network measurements is shown. Infeasibility results with respect to available measurements and faults are also provided, as well as methods to remove faulty agents from the network. Applications to power networks and robotic formations are presented. It is shown how the developed methodology apply to a power network described by the swing equation with a faulty bus. For a multi-robot system, it is illustrated how a faulty robot can be detected and removed.

Fault Detection and Isolation for a Supermarket Refrigeration System – Part Two: Unknown-Input-Observer Method and Its Extension

AbstractThe Fault Detection and Isolation (FDI) using the Unknown Input Observer (UIO) for a supermarket refrigeration system is investigated. The original system's state Tgoods (temp. of the goods) is regarded as a system unknown input in this study, so that the FDI decision is not disturbed by the system uncertainties relevant to this state dynamic and the original system disturbance Qairload (the thermal feature of the air). It has been observed that a single UIO has a very good detection capability for concerned sensor and parametric faults. However, only the parametric fault can be isolated by using a bank of UIOs. Thereby, a complete FDI approach is proposed by combining the Extended-Kalman-Filter (EKF) and UIO methods, after an extensive comparison of KF-, EKF- and UIO-based FDI methods is carried out. The simulation tests show that the complete FDI approach has a good and successful FDI capability regarding to concerned fault scenarios.

Novel robust fault diagnosis method for flight control systems *

A novel robust fault diagnosis scheme, which possesses fault estimate capability as well as fault diagnosis property, is proposed. The scheme is developed based on a suitable combination of the adaptive multiple model (AMM) and unknown input observer (UIO). The main idea of the proposed scheme stems from the fact that the actuator Lock-in-Place fault is unknown (when and where the actuator gets locked are unknown), and multiple models are used to describe different fault scenarios, then a bank of unknown input observers are designed to implement the disturbance de-coupling. According to Lyapunov theory, proof of the robustness of the newly developed scheme in the presence of faults and disturbances is derived. Numerical simulation results on an aircraft example show satisfactory performance of the proposed algorithm.

Multi-leak detection and isolation in fluid pipelines

A multi-leak detection system for pipelines is designed and tested. The multi-leak detection problem is solved using only sensors of flow and pressure at the extremes of the duct, and using the analytical redundancy given of these measurements. The leak detection design is based on a distributed pipeline model that is discretized in space and assumes a set of leaks distributed through the duct. Leak location is accomplished by evaluating the residuals of a bank of unknown input observers that are robust against one leak and sensitive to the rest. Simulation and experimental results are reported to demonstrate the effectiveness of the proposed approach when two leaks appear simultaneously.