Adaptive Unknown Input Observer Research Articles

Sensor Fault Reconstruction Using Robustly Adaptive Unknown-Input Observers.

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies.

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.

Adaptive unknown input observer-based detection and identification method for intelligent transportation under malicious attack

This paper aims at developing a novel detection and identification method against malicious attacks in intelligent transportation. Due to the development and applications of communication and advanced sensor technologies, intelligent transportation has faced new safety risks. In particular, the emerging malicious attacks, such as false data injection attack, can mask the destruction of physical dynamic by tampering with information in layer to fool the current detection methods. Because of this reason, an adaptive unknown input observer-based detection and identification method is developed. Firstly, a physical dynamics model of vehicle networking system is established by considering the actual physical state. Considering the spoofing characteristics of false data injection attack, an unknown input observer-based detection method is proposed. Through the design of adaptive unknown input observer parameters, the detection performance, can be improved by cutting down the state estimation error. Compared with the UIO-based detection method, simulations demonstrate that the false positive rate can be reduced 0.1%. Based on the feature of state residuals that is not sensitive to the attacked ith residual, but sensitive to other residuals, a novel identification criterion is developed. At last, simulation experiments on the Matlab verify the performance of the proposed detection and identification algorithm in intelligent transportation system.

Fast actuator and sensor fault estimation based on adaptive unknown input observer

This study evaluates the robust fault estimation problem of systems with actuator and sensor faults though the simultaneous use of unknown input disturbances and measurement noise. Specifically, an augmented descriptor system is preliminarily developed by creating an augmented state consisting of system states and sensor faults. Next, a novel fast adaptive unknown input observer (FAUIO) is proposed for the system to enhance its fault estimation performance. The existence condition of the novel FAUIO is then introduced for linear time-invariant systems with unknown input disturbances. Furthermore, the proposed FAUIO is extended to a class of Lipschitz nonlinear systems with unknown input disturbances and measurement noise to investigate the robust fault estimation problem. Accordingly, an H∞ performance index is employed to attenuate the influence of disturbances on fault estimation. Moreover, the linear matrix inequality (LMI) technique is applied to solve the designed FAUIO. Finally, the effectiveness of the developed FAUIO is validated via the simulation of two examples.

Adaptive unknown input observer approach for multi-fault diagnosis of PEM fuel cell system with time-delays

An adaptive unknown input observer (AUIO) for polymer electrolyte membrane fuel cell system with time-delays is developed and applied to formulate a fault diagnosis algorithm for multiple actuator fault detection and isolation (MAFDI). In a scheme based on multiple-model, a group of observers are developed based on a model that describes the system in the presence of a specific actuator fault. The observers are used to generate fault-dependant residual signals. When a model matches the system, the residual signal is zero and becomes non-zero in case of a faulty scenario. Two types of actuator faults are considered namely stuck fault, where the inputs delivered by the faulty actuators are blocked to constant value, and loss of effectiveness fault. The effectiveness of the proposed AUIO and MAFDI algorithm is shown with simulation results.

Robust adaptive unknown input observer design for a class of disturbed systems

This paper proposes a novel approach for designing a robust adaptive unknown input observer (UIO) for a class of nonlinear system. The system is composed of nonlinear terms and exogenous disturbance and fault signals which are considered to be unknown. The main contribution of the paper is to present the UIO system that uses adaptive law and linear matrix inequality (LMI). Adaptive law is used for system current fault estimation and LMI is used to provide the feasible set of responses while ensuring system stability. The effectiveness of the proposed method is shown by applying to the numerical examples. The simulation results show the validity of this method for a nonlinear system with unknown upper bound disturbance.

Adaptive unknown input observer for nonlinear systems with application to fault diagnosis

An adaptive unknown input observer for nonlinear systems whose nonlinear functions satisfy Lipschitz condition is presented. The main advantage of the proposed observer is that it is sufficient to satisfy the standard existence conditions of normal unknown input observer design. Another advantage is that it estimates both states and parameters simultaneously. So it can be very much useful for robust control and fault diagnosis. As a possible application, an actuator fault diagnosis scheme is developed for single link robot arm. Finally, numerical simulation is performed to validate the effectiveness of the proposed observer in estimating the parameter along with states.

Fault estimation for a class of interconnected non-linear systems with time-varying delay using robust adaptive unknown input observers

Fault estimation for a class of interconnected non-linear systems with time-varying delay using robust adaptive unknown input observers

Improved FastSLAM2.0 using ANFIS and PSO

Wheeled mobile robots (WMRs) are of great importance. Therefore, it is necessary to make sure that they are not defected. But, in case of failures, the diagnosis task is very important to predict then solve the problem. The most useful techniques in diagnosis are observers which are based on the observability of the monitored system that is not usually ensured by WMR. Thus, to overcome this drawback, an intelligent cooperative diagnosis algorithm is proposed and tested for a group of mobile robots. The diagnosis algorithm is based on robust adaptive unknown input observer applied on unobservable robot. The local non-observability of each robot is solved by cooperative communication. The idea consists on considering all WMRs as a Large Scale System (LSS) even these robots may have not common task. Then, the LSS is decomposed into subsystems that everyone refers to each robot communicating with its neighbors. Next, a design of cooperative interconnected systems is studied to reassure the new condition of observability. Besides, Fast Adaptive Fault Estimation (FAFE) algorithm is proposed to improve the performances of the fault estimation. Finally, to illustrate the efficiency of the proposed algorithm, a model of three-wheel omnidirectional mobile robot is presented.

Incipient sensor fault estimation and accommodation for inverter devices in electric railway traction systems

SummaryThis paper proposes an incipient sensor fault estimation and accommodation method for three‐phase PWM inverter devices in electric railway traction systems. First, the dynamics of inverters and incipient voltage sensor faults are modeled. Then, for the augmented system formed by original inverter system and incipient sensor faults, an optimal adaptive unknown input observer is proposed to estimate the inverter voltages, currents and the incipient sensor faults. The designed observer guarantees that the estimation errors converge to the minimal invariant ellipsoid. Moreover, based on the output regulator via internal model principle, the fault accommodation controller is proposed to ensure that the vod and voq voltages track the desired reference voltages with the tracking error converging to the minimal invariant ellipsoid. Finally, simulations based on the traction system in CRH2 (China Railway High‐speed) are presented to verify the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

Fault tolerant control using adaptive output integral-type sliding mode

This paper proposes a novel fault tolerant control scheme for linear systems with unknown bounded uncertainty and time-varying fault. In this scheme, adaptive output integral-type sliding mode is incorporated with adaptive unknown input observer to cope with aforementioned systems using only input and output information. More specifically, the adaptive unknown input observer is exploited to estimate state and fault simultaneously, and the adaptive output integral-type sliding mode is designed to attenuate unknown bounded uncertainty and tolerate time-varying fault. Then, Lyapunov stability theory is applied to synthesize the design of the observer and nominal controller. Finally, the effectiveness of the proposed method is verified by a numerical example.

Robust actuator fault diagnosis scheme for satellite attitude control systems

In this paper, a robust actuator fault diagnosis scheme is investigated for satellite attitude control systems subject to model uncertainties, space disturbance torques and gyro drifts. A nonlinear unknown input observer is designed to detect the occurrence of any actuator fault. Subsequently, a bank of adaptive unknown input observers activated by the detection results are designed to isolate which actuator is faulty and then estimate of the fault parameter. Fault isolation is achieved based on the well known generalized observer strategy. The simulation on a closed-loop satellite control system with time-varying or constant actuator faults in the form of additive and multiplicative unknown dynamics demonstrates the effectiveness of the proposed robust fault diagnosis strategy.

Continuously-implemented sliding-mode adaptive unknown-input observers under noisy measurements

We propose an estimator for nonlinear systems with unmatched unknown inputs and under measurement noise. The estimator design is based on the combination of observer design for descriptor systems, sliding-modes theory and adaptive control. The estimation of the measurement noise is achieved thanks to the transformation of the original system into a singular form where the measurement noise makes part of the augmented state. Two adaptive parameters are updated online, one to compensate for the unknown bounds on the states, the unknown inputs and the measurement noise and a second one to compensate for the effect of the nonlinearities. To join robust state estimation and unknown-inputs reconstruction, our approach borrows inspiration from sliding-mode theory however, all signals are continuously implemented. We demonstrate that both state and unknown-inputs estimation are achieved up to arbitrarily small tolerance. The utility of our theoretical results is illustrated through simulation case-studies.

A new secured transmission scheme based on chaotic synchronization via smooth adaptive unknown-input observers

We present a new scheme for the secured transmission of information based on master–slave synchronization of chaotic systems, using unknown-input observers. Our approach improves upon state-of-the-art schemes by being compatible with information of relatively large amplitude while improving security against intruders through an intricate encryption system. In addition, our approach is robust to channel noise. The main idea is to separate the encryption and synchronization operations by using two cascaded chaotic systems in the transmitter. Technically, the scheme is based on smooth adaptive unknown-input observers; these have the advantage to estimate the (master) states and to reconstruct the unknown inputs simultaneously. The performance of the communication system is illustrated in numerical simulation.

적응 미지입력 관측기를 이용한 무인항공기의 조종면 구동기 고장진단

본 논문은 항공기의 신뢰도를 높이기 위한 목적으로, 항공기 조종면에 장착된 구동기에 고장이 발생하여 특정위치에 고정되어 움직이지 않을 때, 다중 미지입력 관측기(UIO, Unknown Input Observer)와 적응기법을 이용하여 고장이 발생한 구동기를 구분하고 해당 조종면의 고정된 위치를 추정하여, 조종력 할당 알고리듬 등의 조종면 고장에 대한 재형상 제어를 위한 입력을 제공하는 방법을 다룬다. 구동기의 개수와 동일한 관측기를 구성하되, 각 구동기의 고장이 반영된 선형모델을 사용함으로써 정상인 경우의 관측기에서 출력된 잔차와 고장이 발생한 경우의 관측기에서 출력된 잔차를 비교하여 고장을 분리하고, 적응기법을 이용하여 1초 내에 고장의 정도를 추정한다. 그러나 2개의 동일 형상의 러더가 존재할 경우, 고장분리가 불가함을 밝힌다. In this paper, a parallel bank of multiple adaptive unknown input observers approach suggested by D.Wang is applied to detect a single fault of control surface actuator and to estimate the actuator position of lock-in-place fault using a small fixed-wing UAV model with eight control surfaces. This paper shows that not only the fault diagnosis algorithm detects and estimates each faults of lock-in-place in 1 second by simulation but also it may be unavailable to isolate among two same-shaped rudders.

Adaptive unknown input observer approach for aircraft actuator fault detection and isolation

AbstractIn this paper, an adaptive unknown input observer (UIO) approach is developed to detect and isolate aircraft actuator faults. In a multiple‐model scheme, a bank of parallel observers are constructed, each of which is based on a model that describes the system in the presence of a particular actuator fault. The observers are constructed based on a modified form of the standard UIO to generate fault‐dependant residual signals, such that when a model matches the system, the residual signal will be zero. Otherwise, the residual will be definitely non‐zero and governed uniquely by the faulty signal. For locked actuators and loss of actuator effectiveness, in which the locked position and the reduced effectiveness are additional unknowns, we develop an adaptive scheme to estimate these unknown parameters. To the best of our knowledge, this is the first adaptive UIO presented. We prove that the proposed adaptive algorithms guarantee that both the residual signals and the estimation errors of the unknown parameters converge exponentially when a model matches the plant. By further designing a model‐matching index, the fault can be isolated accurately. A condition for the approach is that for an nth order system, there must be n independent measurements available. This requirement limits the applicability of our proposed approach. The condition is certainly satisfied by all state‐feedback control systems. However, for some other systems, extra efforts may be needed to increase the number of measurements. The method is applied to a linear model of the F‐16 aircraft with controller. The results show that the approach is effective. Copyright © 2006 John Wiley & Sons, Ltd.