Unknown Input Observer Research Articles

A new H∞ observer for continuous LPV systems with unknown inputs using an HOSM differentiator

Abstract The main contribution of this paper is the development of $H_{\infty }$ state and unknown input (UI) observers for noisy linear parameter-varying (LPV) systems. The observers are constructed in order to be unbiased (in particular the state estimation error is decoupled from the UI) and with a minimum $L_{2}$ transfer between the perturbations (that are assumed to be with finite energy) and the estimation errors. Contrary to equivalent observers developed in the literature, the present one relaxes a widely used rank condition on the system matrices for decoupling the UI. In order to do so, high-order derivation is needed, which is done using a high-order sliding modes differentiator. A method is given to design observer gains for LPV systems under polytopic form. Finally, three examples illustrate some aspects of the theoretical contributions, and compare this work to the existing ones.

Fault-tolerant control of the main drive system of rolling mill based on fault reconfiguration

A mathematical model of the main drive system of rolling mill is developed for the motor armature faults in the main drive system of rolling mill, taking into account the effects of nonlinear damping, friction damping between rolls and external perturbations on the system during operation. The unknown input observer is designed to process perturbations and errors as unknown inputs, and the [Formula: see text] performance index is designed to improve the robustness of the unknown input observer to fault reconfiguration. At the same time, in order to ensure the stability of the main drive system of rolling mill after the occurrence of faults, an active fault-tolerant controller is designed for the main drive system of rolling mill after obtaining the accurate fault reconfiguration value, which realizes the active fault-tolerant control of the main drive system of rolling mill. Through the simulation study on the main drive system of 2030 mm cold rolling mill stand F4, it is shown that the system is restored to the normal state after adding the fault-tolerant controller, and the motor angular speed error and roll angular speed error are 2.12% and 2.43%, respectively. Comparing with the fault-tolerant control method based on fault estimation, the root-mean-square errors of the motor angular speed and roll angular speed estimates of the designed fault-tolerant control method are reduced by 4.24% and 3.28%, respectively. Simulation verifies the effectiveness of the proposed method.

Revisiting the observability and identifiability properties of a popular HIV model

This paper revisits the observability and identifiability properties of a popular ODE model commonly adopted to characterize the HIV dynamics in HIV-infected patients with antiretroviral treatment. These properties are determined by using the general analytical solution of the unknown input observability problem, introduced very recently in Martinelli (2022). This solution provides the systematic procedures able to determine the state observability and the parameter identifiability of any ODE model, in particular, even in the presence of time varying parameters. Four variants of the HIV model are investigated. They differ because some of their parameters are considered constant or time varying. Fundamental new properties, which also highlight an error in the scientific literature, are automatically determined and discussed. Additionally, for each variant, the paper provides a quantitative answer to the following practical question: What is the minimal external information (external to the available measurements of the system outputs) required to make observable the state and identifiable all the model parameters? The answer to this fundamental question is obtained by exploiting the concept of continuous symmetry, recently introduced in Martinelli (2019). This concept allows us to determine a first preliminary general result which is then applied to the HIV model. Finally, for each variant, the paper concludes by providing a redefinition of the state and of the parameters in order to obtain a full description of the system only in terms of a state which is observable and a set of parameters which are identifiable (both constant and time varying).

Convex unknown input observer for sensor fault estimation of nonlinear descriptor systems via linear matrix inequalities

AbstractThis paper proposes a novel convex unknown input observer for simultaneously estimating system states and sensor faults in nonlinear systems. The observer design is based on the Lyapunov method and provides conditions in the form of linear matrix inequalities. The methodology allows handling a larger family of systems than previous approaches as it considers unknown premise variables for the convex error system. Numerical simulations and real‐time experiments demonstrate the effectiveness in practical applications. The results suggest that the proposed convex observer can accurately estimate system states and sensor faults.

Research on fault diagnosis of electromagnetic active suspension based on double observer intelligent switching

Electromagnetic active suspension can adjust stiffness and damping and is widely used in suspension. When the electromagnetic active suspension fails, the observer can be used for fault detection. In order to realize suspension fault detection under all road conditions, a double observer switching strategy is designed and simulated using MATLAB. Firstly, the switching strategies of the Kalman observer, unknown input observer (UIO) and double observer are designed, respectively. Then, for random roads, the Kalman observer is used for fault detection, while for bump road surfaces, the UIO is used for fault detection. The residoubles of the dynamic deflection of the suspension are used as switching conditions. Finally, a simulation analysis is carried out with MATLAB, and the conclusion shows that the switching strategy of double observers is better than that of the Kalman observer and UIO in full road conditions.

Observer‐based output feedback event‐triggered consensus tracking for linear multi‐agent systems

AbstractThis paper investigates the consensus tracking of multi‐agent systems (MASs) with general linear dynamics and directed graphs via observer‐based event‐triggered (ET) control. For each follower, a dynamic event‐triggered (DET) unknown input observer (UIO) is constructed to estimate the relative states, which utilizes the discrete relative output information among neighboring agents, and the estimate errors can exponentially converge to zero. Then, an observer‐based DET controller, which has the superiority of reducing the communication burden, is presented. Unlike most existing works, a dual ET mechanism of observer and controller is proposed, whose triggering functions are independent of each other. In addition, the time‐varying item in the triggering functions is further extended to be a class of positive functions, including some existing exponential functions as its special cases. Under the proposed DET UIO and DET control protocol, it is rigorously demonstrated that consensus tracking can be achieved asymptotically, and Zeno behavior is ruled out. Finally, a numerical example is provided to verify the validity of the results.

Observer based fault diagnosis of aircraft active landing gear system

A landing gear system is vital in aircraft landing and taxiing. In this paper, an observer-based fault detection and fault reconstruction means is adopted for the aircraft’s active landing gear. Firstly, a six-degree-of-freedom (6-DOF) landing gear model is established by analysing the dynamics of the fuselage and landing gears. By analysing the fault mechanism of actuators and sensors, the fault model of actuators and sensors is established. Secondly, according to the above model, the unknown input observer (UIO) is designed to obtain the residuals, and the fault detection of actuators and sensors is realized according to the comparison between residuals and thresholds. On this basis, an adaptive unknown input observer (AUIO) is designed, through which the state values and fault values can be reconstructed. Finally, simulation experiments are tested under different fault types to prove the validity of the fault detection and reconstruction based on UIO and AUIO.

A Comprehensive Fault Detection and Isolation Method for DC Microgrids Using Reduced-Order Unknown Input Observers

Fault diagnosis is of critical importance to the safety of power electronic devices in DC microgrids. To detect and isolate different component faults in DC microgrids, this paper introduces a comprehensive protection scheme using reduced-order unknown input observers (ROUIOs). As opposed to conventional protection strategies, the proposed method provides a centralized fault detection and isolation (FDI) solution for DC microgrids that covers multiple faults occurring in different components in a unified process. Moreover, it reduces the complexity of observer model and relaxes the requirements of measurement signals compared with existing observer-based FDI methods for DC microgrids. To this end, the state-space model of a multi-terminal DC microgrid with different faults is first established. On this basis, a bank of ROUIOs are designed with the aim of classifying different component faults in the system. At last, the performance of the proposed FDI method is verified through numerical simulations with MATLAB/Simulink and hardware tests. Test results show that the proposed method can accurately detect and isolate different component faults in DC microgrids in a short response time of 1 ms.

Fault estimation for nonlinear uncertain time‐delay systems based on unknown input observer

AbstractIn this paper, a novel nonlinear unknown input observer is proposed in order to fault estimation for nonlinear uncertain systems with time delays. By the estimation of the faults, the features are detected such as shape, size occurrence time etc. The time delay is considered a constant and known parameter in the states. The disturbances are investigated in the states and outputs and also, and sensor and actuator faults are considered. The stability of the closed‐loop system is guaranteed by Lyapunov–Krasovskii theory and some feasible Linear matrix inequalities (LMI). The proposed method is simulated on a continuous‐stirred tank reactor (CSTR) with uncertainties and time delay. Simulation results show the appropriate efficiency of the proposed method.

Attack on communication links analysis and detection over the clustered networks

This research focuses on the subject of attack analysis, detection, and mitigation in a network of MASs with linear dynamics. This network is divided into multiple clusters that are disconnected from one another. A leader is an agent that exists in each cluster and has the ability to interact with other leaders through a strongly connected directed graph at discrete instances. Firstly, we analyze the effect of communication link attacks on the consensus algorithm. We show that the consensus control law may be disturbed by a single attack on a communication channel using principles from matrix theory and graph theory. Then, a bank of impulsive unknown input observers is established to ensure each agent is able to detect the communication link attack. Moreover, a modification of the consensus algorithm of MASs based on an estimated attack is also given. Finally, the results of our research are illustrated using simulations.

Unknown Input Observer Scheme for Open-flow Irrigation Channels

In agriculture, most of the water is transported by networks of open channel irrigation systems (OCIS). To ensure an optimal operation of this type of systems, it is very important to have accurate monitoring of all the system state variables. In order to perform robust estimation mitigating the effect of unknown user demands, an unknown input observer for one sided Lipschitz (OSL) quadratically inner bounded (QIB) systems is proposed. The proposed UIO scheme is assessed in a case study based on the Corning channel in California that is modelled uisng material and energy balances. The modeling methodology includes inflows and outflows of matter, along with a transition flow which models the effect of potential energy (channel slope) and kinetic energy (velocity in the transport of matter and frictional losses). The objective of this paper is to show that with the UIO for QIB-OSL systems, costs could be reduced in the purchase of sensors, since the observer is capable of reconstructing the upstream heights from the measurements of the downstream heights, which will be subject to noise and unknown inputs (in this case, user demands).

Cyberattack Detection by Using a Discrete-Time Model-Based Unknown Input Observer

Cyberattack Detection by Using a Discrete-Time Model-Based Unknown Input Observer

Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks.

The advent of smart grid technologies has brought about a paradigm shift in the management and operation of distribution networks, allowing for intricate system information to be encapsulated within semantic network models. These models, while robust, are not immune to faults within their knowledge entities, which can arise from a myriad of issues, potentially leading to verification failures and operational disruptions. Addressing this critical vulnerability, our research delves into the development of a novel fault detection methodology specifically tailored for the knowledge entity variables of semantic networks in distribution networks. In our approach, we first construct a state space equation that models the behavior of knowledge entity variables in the presence of faults. This foundational framework enables us to apply an unknown input observer strategy to effectively detect anomalies within the system. To bolster the fault identification process, we introduce the innovative use of a siamese network, a neural network architecture which is proficient in differentiating between similar datasets. Through simulation scenarios, we demonstrate the efficacy of our proposed fault detection method.

Distributed Interval Observer and Distributed Unknown Input Observer Designs

Distributed Interval Observer and Distributed Unknown Input Observer Designs

A Novel Cyber-Attack Detection and Mitigation for Coupled Power and Information Networks in Microgrids Using Distributed Sliding Mode Unknown Input Observer

A Novel Cyber-Attack Detection and Mitigation for Coupled Power and Information Networks in Microgrids Using Distributed Sliding Mode Unknown Input Observer

On the equivalence of model-based and data-driven approaches to the design of unknown-input observers

On the equivalence of model-based and data-driven approaches to the design of unknown-input observers

Finite-time sliding-mode observer based distributed robust fault tolerant control for multi-agent systems against actuator and sensor faults

The faults of actuators and sensors have adverse impacts on the distributed control of the multi-agent systems (MASs). This study addresses the problem of fault-tolerant control (FTC) for MAS subject to actuator and sensor faults. The descriptor approach is introduced to transform the MAS dynamics into a descriptor form, in which the actuator and sensor faults are integrated as components of the extended descriptor system state. A sliding mode-based unknown input observer is designed to realise finite-time fault diagnosis and state estimation for the transformed descriptor system. Furthermore, based on the estimation, a distributed robust FTC protocol is developed to guarantee the consensus of MAS against the actuator and sensor faults. Finally, simulations are conducted to validate the effectiveness of the proposed approach.

Observer-based consensus of multi-agent systems under odd distributed impulsive control protocol

In this paper, for leader-follower Lipschitz nonlinear multi-agent systems (MASs) affected by external disturbances, observer-based distributed controller design problems are discussed in order to realize the MASs consensus. Firstly, to deal with unmeasured system states and the unknown disturbances, for each follower, a joint unknown input observer is proposed. To do so, through an interval observer, a mathematical relationship between the state and the disturbance is set up in advance. Secondarily, by using the estimations of the state and the disturbance, a local feedback controller is designed aiming at stabilizing the linear part of each follower. Thirdly, an odd distributed impulsive control method is proposed which is utilized to deal with the nonlinearity. Then the MASs consensus goal is realized by combining the local feedback controller and the distributed impulsive controller. At last, a simulation is discussed so as to display the effectivity of the raised scheme.

Secure Estimation, Attack Isolation, and Reconstruction Based on Zonotopic Unknown Input Observer

In this paper, the issues of secure estimation, attack reconstruction and isolation are addressed for cyber-physical systems (CPSs) in the presence of malicious attacks. It is assumed that disturbances and noises are unknown-but-bounded (UBB). Based on different rank constraints, both zonotopic completely unknown input observer (ZCUIO) and zonotopic partially unknown input observer (ZPUIO) are designed to estimate system states. Then, malicious attacks are reconstructed and isolated based on the proposed zonotopic observers. By utilizing switching technique, the corresponding feedback controllers are designed, which can guarantee that the closed-loop system is stable. Finally, numerical simulations are provided to illustrate the validity of the presented approach.

An Active Detection Strategy Based on Dimensionality Reduction for False Data Injection Attacks in Cyber-Physical Systems

In recent years, different intelligent and undetectable cyber-attacks against networked control systems (e.g., replay and covert attacks) have been investigated. In this paper, we design a novel control architecture that prevents their existence. In particular, first, we propose to encode the sensor outputs into a randomly changing lower-dimensional space obtained by means of principal component analysis. Such a transformation ensures optimal information reconstruction on the controller's side, and it prevents the attacker from accessing the original sensor measurements, nullifying the possibility of perfect stealthy attacks. Then, on the controller's side, we design a passive Gaussian anomaly detector that leverages the output of an unknown input observer ad-hoc designed to estimate the system's states and unknown inputs simultaneously. It is formally shown that the proposed detection strategy is able to discover the presence of replay and covert attacks. Simulation results obtained considering a quadruple water tank system confirm the capability of the developed architecture.