High-Gain Estimator and Fault-Tolerant Design With Application to a Gas Turbine Dynamic System

Detection, isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

The problem of fault tolerant shape control for particle size distribution (PSD) process subject to simultaneous time‐varying actuator and sensor faults is studied. In this work, sensor and actuator faults are taken into consideration in a unified framework, and estimated by using adaptive observer technique. For the PSD process, the output distribution, rather than the system output signal itself, is adopted for shape control. Square root rational B‐spline approximation is used to fit the distribution shape. An innovative on‐line fault estimation scheme is proposed for simultaneous actuator and sensor faults estimation. Then an augmented controller based on the virtual actuator and virtual sensor techniques is designed to compensate for the faults and realise PSD shape tracking. A simulation example of emulsion polymerisation is used to validate the proposed scheme and satisfactory performance is obtained.

This paper develops a novel fault reconstruction (FR) method and an FR-based fault-tolerant control (FTC) scheme for systems suffering from both sensor and actuator faults based on the combination of a Luenberger-like reduced-order observer and an interval observer. Firstly, by introducing an output transformation, an auxiliary output that is able to decouple the sensor fault is obtained. Secondly, for addressing the external disturbance and actuator fault, a multiple unknown input (MUI) is formed, and a reduced-order observer that is able to decouple the MUI is constructed. Consequently, asymptotic convergence estimations of the state and the sensor fault can be accomplished. Thirdly, in order to obtain the asymptotic convergence actual FR (AFR), an interval observer is designed. After this, an algebraic connection of the MUI and the state error estimation is given, and, based on the algebraic relationship, an algebraic MUI reconstruction (MUIR) method is proposed. Finally, an FTC scheme is developed by using the state estimation and MUIR. Under the FTC, the closed-loop system is asymptotically stable even if it suffers from sensor and actuator faults simultaneously. Theoretical analysis demonstrates that the observer-based FTC mechanism satisfies the separation principle. At last, two simulation examples are given to verify the effectiveness of the proposed methods.

A vehicle health monitoring system based on analytical redundancy, is developed for automated passenger vehicles. A residual generator and a residual processor are designed together to detect and identify actuator and sensor faults of the Buick LeSabre rapidly. The residual generator includes fault detection filters and parity equations. It uses the control commands and sensor measurements to generate the residuals, which have a unique static pattern in response to each fault. Then, the residual processor interrogates the residuals by matching them to one of several known patterns. It computes the probability of each hypothesis conditioned on the history of residuals. The fault detection latency is reduced by integrating the design of the residual generator and the residual processor. The vehicle health monitoring system is evaluated in real-time on a Buick LeSabre. The vehicle sensor and actuator faults are simulated artificially by the computer or created manually by the driver. In one experiment, a real intermittent sensor fault occurred and was immediately detected and identified. The real-time evaluation demonstrates that the vehicle health monitoring system can detect and identify actuator and sensor faults under various disturbances and uncertainties with almost minimal detection latency.

This paper focuses on insufficient perception in blind scenes and the failures of the actuator and sensor of vehicle suspension. The safe speed is designed and the fault-tolerant strategy based on the observer is designed when the actuator and sensor fail simultaneously. A speed planning strategy to ensure that vehicles operate at a safe speed is proposed. The Markov suspension fault jump model considering the suspension sensor and actuator faults is established. Based on the fault estimation obtained by the fault observer, a fault tolerance control strategy of sensor signal reconstruction and actuator fault compensation is adopted. The measurement index for evaluating fault tolerance effectiveness has been established, and an exploration of fault tolerance control is conducted across various fault levels, to identify the preferred fault tolerance strategies corresponding to different levels of fault tolerance. The simulation results show that the designed speed control strategy can improve vehicle safety by decelerating in advance, the fault observer is capable of accurately estimating the true values of actuator and sensor faults. The simulation and vehicle test results demonstrate that similar suspension performance has been obtained by fault-tolerant control strategy and preferred fault tolerance under different fault tolerance levels compared with normal fault tolerance.

This paper addresses the problem of simultaneously estimating actuator and sensor faults of Lipschitz nonlinear systems with non-parametric uncertainties. The proposed fault estimation scheme initially takes sensor faults as auxiliary states and an augmented descriptor system is constructed. By designing a modified proportional and derivative (PD) observer with an adaptive law for this system, the estimation of the original system states, sensor faults and actuator faults can be obtained simultaneously. The sufficient condition for stability of the proposed observer with ℋ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> performance has been derived based on Lyapunov theory. The stability condition is expressed as Linear Matrix Inequality (LMI) optimization problem for minimizing the ℋ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> norm of transfer matrix between the estimation error and uncertainties, which outlines a constructive design procedure for observer parameters. It is shown from the simulation that the proposed approach is capable of successfully estimating states and faults not only for nonlinear state-space systems, but also for nonlinear descriptor systems.

An independent radial basis function neural network (RBFNN) is developed and employed here for an online diagnosis of actuator and sensor faults. In this research, a robust fault detection and isolation scheme is developed for an open-loop exothermic semi-batch polymerization reactor described by Chylla–Haase. The independent RBFNN is employed here for online diagnosis of faults when the system is subjected to system uncertainties and disturbances. Two different techniques to employ RBFNNs are investigated. Firstly, an independent neural network (NN) is used to model the reactor dynamics and generate residuals. Secondly, an additional RBFNN is developed as a classifier to isolate faults from the generated residuals. Three sensor faults and one actuator fault are simulated on the reactor. Moreover, many practical disturbances and system uncertainties, such as monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise, are modelled. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

ABSTRACTIn this paper, a fault estimation and fault-tolerant control problem for a class of T-S fuzzy stochastic time-delay systems with actuator and sensor faults is investigated. A novel sliding mode observer is proposed, which can simultaneously estimate the system states, actuator and sensor faults with good accuracy. Based on the state and actuator fault estimation, a new sliding mode control scheme is developed, which can effectively eliminate the influence of actuator fault. Sufficient conditions for the existence of the proposed observer and fault-tolerant sliding mode controller are provided in terms of linear matrix inequality, and moreover, the reachability of the sliding mode surface can be guaranteed under the proposed control scheme. The propose sliding mode observer and fault-tolerant sliding mode controller can overcome the restrictive assumption that the input matrix of all local modes is the same. Finally, a numerical example is provided to verify the effectiveness of the proposed sliding mode observer and fault-tolerant sliding mode control technique.

In this study, we simultaneously evaluate the multiple‐fault diagnosis problem of a class of Lipschitz nonlinear systems with actuator and sensor faults and unknown input disturbances. A nonsingular system transformation is used to transform the original system into two subsystems for multiple‐fault diagnosis: subsystems 1 and 2. At the system level, two robust sliding‐mode observers (RSMOs) are proposed. An RSMO is designed for subsystem 1 to detect actuator faults subjected to unknown input disturbances, and another RSMO is designed for subsystem 2 to detect sensor faults subjected to actuator faults. At the component level, a bank of RSMOs is proposed to detect and isolate actuators (sensors) with faults using a dedicated observer scheme. The reachability of RSMOs is comprehensively investigated in the estimation error space. Accordingly, the proposed observer parameters are designed as an optimization problem and solved using the linear matrix inequality (LMI) optimization technique. The effectiveness of the proposed multiple‐fault diagnosis scheme was validated through simulations of a modified seventh‐order aircraft system.

This paper presents the concept of a convoy of autonomous electric vehicles which will be able to continue its mission in the presence of some actuator or sensor faults. The autonomous vehicle is called RobuCar which is constituted by four independently driven wheels and two independently adjustable steering angles. Firstly, the authors develop longitudinal and lateral controllers for the vehicles operating normally. The objective is to follow a reference trajectory and to maintain safe spacing between vehicles. Secondly, the authors develop some strategies based on fault detection and isolation results in order to reconfigure the convoy of vehicles in presence of faults. Three categories have been considered: sensor or actuator fault which has not altered the trajectory; fault that affects the velocity or disturbs the trajectory; faults that make impossible for the vehicle to continue its mission. Simulation results are given at the end to show the efficiency of the approach.

Robust fault detection filter (RFDF) is mainly designed to detect faults in linear time-invariant (LTI) systems inherently exposed to external disturbances. H-index norm technique is one of the RFDF designing methods. The main idea of our study is to apply a continuous H-index method to a real boiler model which is made proper by adding an auxiliary direct channel. A high pass filter is augmented to raise the high frequency response. The H-index norm is maximized to distinguish between external disturbance and fault. The designed RFDF is continuous and can directly be implemented to the time-domain original continuous model. Furthermore, discretizing the continuous model may cause losing some information. Finally, the designed method is theoretically applied in a simulated model of a drum boiler operating in Synvendska Kraft AB Plant in Malmo, Sweden as a multivariable and strongly coupled system. It is illustrated that both sensor and actuator faults can robustly be detected as quickly as possible. To the best of authors' knowledge, this is the first time that the continuous H-index norm procedure is employed to detect actuator and sensor faults in a boiler model.

This paper aims at providing two competitive solutions to the problem of estimating state as well as sensor and actuator faults. The development starts with a common system description, which is transformed into a nonlinear descriptor system-like form. The appealing property of such an approach is that the sensor fault is eliminated from the output equation by suitably extending the state vector. Subsequently, a novel fault estimation scheme is proposed and its error dynamics are carefully described. Having such a unified framework, two competitive design procedures are proposed. One is based on the celebrated H_&#x221E; while the other employs the quadratic boundedness strategy. With the estimators, the paper provides a unified framework for assessing the uncertainty of the resulting estimates in the form of uncertainty intervals. Thus, both solutions can be compared from the theoretical perspective. The final part of the paper contains a numerical simulation example concerning a twin-rotor system that clearly shows the comparison and performance of the above approaches. Subsequently, an experimental study concerning a multi-tank system using real data is reported.

Design Method of Fault Detector for Injection Unit

This paper focuses on multiple delay-dependent noise-to-state stability (NSS) for a class of switched random nonlinear systems against uncertainty terms and intermittent sensor and actuator faults. External disturbances, nonlinear functions, as well as measurement noise, are also taken into account. This is the first attempt to achieve dynamic output feedback controller design for uncertain switched random nonlinear systems subject to intermittent sensor and actuator faults. First, a controller is established to perform passive fault-tolerant control (FTC). Random systems are more common than Ito stochastic systems. Thus, compared with the previous works, the proposed controller has a wider application scope and is more feasible. Next, an augmented closed-loop system is exhibited to realize NSS. Moreover, a piecewise Lyapunov function is utilized with less conservatism than common Lyapunov function. The delay dependent stability conditions are gathered via linear matrix inequalities (LMIs) and controller matrices are earned. At last, the novelty and validity of the approach suggested in this paper are demonstrated through two simulation examples.

In this paper, a model-based approach to detect and to isolate sensors and actuators faults using nonlinear sliding mode observers is proposed for an actuated seat. The goal is to ensure the comfort and the security of the users in simulators applications. The principle is to reconstruct the state vector of the system by sliding mode observers and to compare the estimated outputs with those measured as residuals. In this work, a multi-observers technique is used. It consists of designing multiple observers such that each observer must be robust to noises and to other uncertainties but sensitive to each actuator or sensor fault. Simulation results are given to show the effectiveness of the approach.