A Decoupled Parameters Estimators for in Nonlinear Systems Fault diagnosis by ANFIS

This paper presents a new and efficient Adaptive Neural Fuzzy Inference Systems approach for satellite’s attitude control systems (ACSs) fault diagnosis. The proposed approach formulates the fault modelling problem of system component into an on-line parameters estimation The learning  ability of the adaptive neural fuzzy inference system allow as to decoupling the effect of each fault from the estimation of the others.  Our solution provides a method to detect, isolate, and estimate various faults in system components, using Adaptive Fuzzy Inference Systems Parameter Estimators (ANFISPEs) that are designed and based on parameterizations related to each class of fault. Each ANFISPE estimates the corresponding unknown Fault Parameter (FP) that is further used for fault detection, isolation and identification purposes. Simulation results reveal the effectiveness of the developed FDI scheme of an ACSs actuators of a 3-axis stabilized satellite.DOI:http://dx.doi.org/10.11591/ijece.v2i2.22

Fault detection, isolation, and identification for nonlinear systems using a hybrid approach

This thesis presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems; taking advantage of both system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution are a bank of adaptive neural parameter estimators (NPE) and a set of single-parameterized fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. In view of the availability of full-state measurements, two NPE structures, namely series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Simple neural network architecture and update laws make both schemes suitable for real-time implementations. A fault tolerant observer (FTO) is then designed to extend the FDII schemes to systems with partial-state measurement. The proposed FTO is a neural state estimator that can estimate unmeasured states even in presence of faults. The estimated and the measured states then comprise the inputs to the FDII schemes. Simulation results for FDII of reaction wheels of a 3-axis stabilized satellite in presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solution under both full and partial-state measurements

Model-based fault diagnosis for aerospace systems: a survey

http://pig.sagepub.com/content/early/2012/01/06/0954410011421717International audienceThis survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic nonlinear dynamical modeling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided

Fault-tolerant control strategies for a class of Euler-Lagrange nonlinear systems subject to simultaneous sensor and actuator faults

The problem of Fault Detection, Isolation, and Estimation (FDIE) as well as Fault-Tolerant Control (FTC) for a class of nonlinear systems modeled with Euler-Lagrange (EL) equations subjected to simultaneous sensor and actuator faults are considered in this study. To tackle this problem, first state and output linear transformations are introduced to decouple the effects of sensor and actuator faults. These transformations do not depend on the system nonlinearities. An analytical procedure based on two Linear Matrix Inequality (LMI) feasibility conditions is proposed to obtain these transformations. Once, the effects of faults are decoupled, two Sliding Mode Observers (SMO) are designed to reconstruct each type of fault, separately. Subsequently, the results of fault estimations are fed back to the controller and the effects of faults are compensated for. In this study, the mathematical stability proof of the coupled controller, observers, and the nonlinear system is provided. Unlike previous methodologies in the literature, no limiting assumptions such as Lipschitz conditions are imposed on the system. Next, a novel fault tolerant control scheme is proposed in which a single SMO is used to reconstruct sensor faults and provide a compensation term to rectify the effects of faults. However, to deal with actuator faults, a Sliding Mode Controller (SMC) is employed. Using this robust FTC technique, zero tracking error in the presence of uncertainties, measurement noise, disturbances, and faults as well as estimation of the actuator faults are possible. The stability proof for the coupled nonlinear controller, observer and plant is provided by using the properties of Euler-Lagrange equations and sliding mode techniques. Finally, to evaluate the performance of the proposed FDIE and FTC approaches, extensive sets of simulations are performed on a 3 Degrees Of Freedom (DOF) Autonomous Underwater Vehicle (AUV). Simulation studies show the promising results obtained as a result of the presented approaches as compared to those obtained by using the existing methodologies

Joint University Program for Air Transportation Research, 1991-1992

This report summarizes the research conducted during the academic year 1991-1992 under the FAA/NASA sponsored Joint University Program for Air Transportation Research. The year end review was held at Ohio University, Athens, Ohio, June 18-19, 1992. The Joint University Program is a coordinated set of three grants sponsored by the Federal Aviation Administration and NASA Langley Research Center, one each with the Massachusetts Institute of Technology (NGL-22-009-640), Ohio University (NGR-36-009-017), and Princeton University (NGL-31-001-252). Completed works, status reports, and annotated bibliographies are presented for research topics, which include navigation, guidance and control theory and practice, intelligent flight control, flight dynamics, human factors, and air traffic control processes. An overview of the year's activities for each university is also presented

Fault Detection, Isolation and Identification of Formation Flying Satellites using Wavelet-Entropy and Neural Networks

The main objective of this thesis is to develop a fault detection, isolation and identification (FDII) scheme based on Wavelet Entropy (WE) and Artificial Neural Network (ANN) for reaction wheels (RW) that are employed as actuators in the attitude control subsystem (ACS) of a satellites to perform the formation flying (FF) missions. In this thesis two FDII approaches are proposed, i) Spacecraft-level fault diagnosis and ii) Formation-level fault diagnosis. In the "spacecraft-level" fault diagnosis scheme in order to analysis faults, absolute attitude and angular measurements from a satellite are considered as diagnostic signals. In order to detect the fault, the wavelet-entropy technique is employed on diagnostic signals and the sum of the absolute wavelet entropies (SAWE) of the diagnostic signals are obtained and compared with an appropriately selected threshold. If the SAWE passes the threshold the faulty condition is established. In order to isolate the fault in a satellite the angular velocity measurements in a satellite are considered as diagnostic signals and the relative wavelet energy (RWE) of these signals are obtained and compared to a threshold. In our proposed fault identification scheme, the attitude measurements in a satellite are considered and the detail and approximation coefficients of the wavelet signals are obtained and these coefficients are used as inputs to an artificial neural network to identify the type of the fault in a satellite. Using a confusion matrix evaluation system we demonstrate that our spacecraft-level FDII can detect, isolate and identify the high severity faults in a satellite however this scheme cannot detect low severity faults in a satellite. Our proposed "formation-level" FDII scheme utilizes data collected from the relative attitudes and relative angular velocity measurements of the formation flying satellites. In this fault diagnosis scheme, the relative attitude and relative angular velocity measurements in a satellite with respect to each its neighbor's in a formation are considered as diagnostic signals. In order to detect the fault, the relative attitude measurements in a satellite are considered as diagnostic signals. The wavelet-entropy technique is utilized on diagnostic signals and the SAWEs with respect to each satellite's neighbor are obtained. These SAWEs are then compared with an appropriately selected threshold. The faulty satellite is determined if these SAWEs pass the thresholds. In order to isolate the fault in a faulty satellite, the relative angular velocity measurements are considered as diagnostic signals. The RWE of these signals are obtained and compared to a threshold. In our proposed fault identification scheme, the relative attitude measurements in a satellite are considered as diagnostic signals. In this scheme, the RWEs of the diagnostic signals are obtained and used as inputs to an artificial neural network to identify the type of the fault in a satellite. According to the simulation results, our proposed FDII scheme can detect, isolate and identified both low severity and high severity faults in the reaction wheels of satellite

Fault Detection and Isolation in Attitude Control Subsystem of Spacecraft Formation Flying using Extended Kalman Filters

In this thesis, the problem of fault detection and isolation in the attitude control subsystem of spacecraft formation flying is considered. For this purpose, first the attitude dynamics of a single spacecraft is analyzed and a nonlinear model is defined for our problem. This is followed up by generating the model of the spacecraft formation flight using the attitude model and controlling the formation based on virtual structure control scheme. In order to design the fault detection method, an extended Kalman filter is utilized which is a nonlinear stochastic state estimation method. Three fault detection architectures, namely, centralized, decentralized, and semi-decentralized are designed based on extended Kalman filters. Moreover, the `residual generation and threshold selection techniques are proposed for these architectures. The capabilities of the architectures for fault detection are studied through extensive numerical simulations. Using a confusion matrix evaluation system, it is shown that the centralized architecture can achieve the most reliable results relative to the semi-decentralized and decentralized architectures. Furthermore, the results confirm that the fault detection in formations with angular velocity measurements achieve higher level of accuracy, true faulty, and precision, along with lower level of false healthy misclassification as compared to the formations with only attitude measurements. In order to isolate the faults, structured residuals are designed for the decentralized, semi-decentralized, and centralized architectures. By using the confusion matrix tables, the results from each isolation technique are presented for different fault scenarios. Finally, based on the comparisons made among the architectures, it is shown that the centralized architecture has the highest accuracy in isolating the faults in the formations. Furthermore, the results confirm that fault isolation in formations with angular velocity measurements achieve higher level of accuracy when compared to formations with only attitude measurements

Active fault-tolerant control of nonlinear systems with wind turbine application

The thesis concerns the theoretical development of Active Fault-Tolerant Control (AFTC) methods for nonlinear system via T-S multiple-modelling approach. The thesis adopted the estimation and compensation approach to AFTC within a tracking control framework. In this framework, the thesis considers several approaches to robust T-S fuzzy control and T-S fuzzy estimation: T-S fuzzy proportional multiple integral observer (PMIO); T-S fuzzy proportional-proportional integral observer (PPIO); T-S fuzzy virtual sensor (VS) based AFTC; T-S fuzzy Dynamic Output Feedback Control TSDOFC; T-S observer-based feedback control; Sliding Mode Control (SMC). The theoretical concepts have been applied to an offshore wind turbine (OWT) application study. The key developments that present in this thesis are:• The development of three active Fault Tolerant Tracking Control (FTTC) strategies for nonlinear systems described via T-S fuzzy inference modelling. The proposals combine the use of Linear Reference Model Fuzzy Control (LRMFC) with either the estimation and compensation concept or the control reconfiguration concept.• The development of T-S fuzzy observer-based state estimate fuzzy control strategy for nonlinear systems. The developed strategy has the capability to tolerate simultaneous actuator and sensor faults within tracking and regulating control framework. Additionally, a proposal to recover the Separation Principle has also been developed via the use of TSDOFC within the FTTC framework.• The proposals of two FTTC strategies based on the estimation and compensation concept for sustainable OWTs control. The proposals have introduced a significant attribute to the literature of sustainable OWTs control via (1) Obviating the need for Fault Detection and Diagnosis (FDD) unit, (2) Providing useful information to evaluate fault severity via the fault estimation signals.• The development of FTTC architecture for OWTs that combines the use of TSDOFC and a form of cascaded observers (cascaded analytical redundancy). This architecture is proposed in order to ensure the robustness of both the TSDOFC and the EWS estimator against the generator and rotor speed sensor faults.• A sliding mode baseline controller has been proposed within three FTTC strategies for sustainable OWTs control. The proposals utilise the inherent robustness of the SMC to tolerate some matched faults without the need for analytical redundancy. Following this, the combination of SMC and estimation and compensation framework proposed to ensure the close-loop system robustness to various faults.• Within the framework of the developed T-S fuzzy based FTTC strategies, a new perspective to reduce the T-S fuzzy control design conservatism problem has been proposed via the use of different control techniques that demand less design constraints. Moreover, within the SMC based FTTC, an investigation is given to demonstrate the SMC robustness against a wider than usual set of faults is enhanced via designing the sliding surface with minimum dimension of the feedback signals