An Adaptive Approach to Sensor Bias Fault Diagnosis and Accommodation for a Class of Input-Output Nonlinear Systems

This paper presents an adaptive sensor fault diagnosis and accommodation scheme for multiple sensor bias faults for a class of input-output nonlinear systems subject to modeling uncertainty and measurement noise. The proposed scheme consists of a nonlinear estimation model that includes an adaptive component which is initiated upon the detection of a fault, in order to approximate the magnitude of the bias faults. A detectability condition characterizing the class of detectable sensor bias faults is derived and the robustness and stability properties of the adaptive scheme are presented. The estimation of the magnitude of the sensor bias faults allows the identification of the faulty sensors and it is also used for fault accommodation purposes. The effectiveness of the proposed scheme is demonstrated through a simulation example

Real-time fault diagnosis for propulsion systems

Current research toward real time fault diagnosis for propulsion systems at NASA-Lewis is described. The research is being applied to both air breathing and rocket propulsion systems. Topics include fault detection methods including neural networks, system modeling, and real time implementations

Distributed Fault Diagnosis of Interconnected Nonlinear Uncertain Systems

Fault diagnosis is crucial in achieving safe and reliable operations of interconnected control systems. This dissertation presents a distributed fault detection and isolation (FDI) method for interconnected nonlinear uncertain systems. The contributions of this dissertation include the following: First, the detection and isolation problem of process faults in a class of interconnected input-output nonlinear uncertain systems is investigated. A novel fault detection and isolation scheme is devised, and the fault detectability and isolability conditions are rigorously investigated, characterizing the class of faults in each subsystem that are detectable and isolable by the proposed distributed FDI method. Second, a distributed sensor fault FDI scheme is developed in a class of interconnected input-output nonlinear systems where only the measurable part of state variables are directly affected by the interconnections between subsystems. A class of multimachine power systems is used as an application example to illustrate the effectiveness of the proposed approach. Third, the previous results are extended to a class of interconnected input-output nonlinear systems where both the unknown and the measurable part of system states of each subsystem are directly affected by the interconnections between subsystems. In this case, the fault propagation effect among subsystems directly affects the unknown part of state variables of each subsystem. Thus, the problem considered is more challenging than what is described above. Finally, a fault detection scheme is presented for a more general distributed nonlinear systems. With a removal of a restrictive limitation on the system model structure, the results described above are extended to a class of interconnected nonlinear uncertain systems with a more general structure. In addition, the effectiveness of the above fault diagnosis schemes is illustrated by using simulations of interconnected inverted pendulums mounted on carts and multi-machine power systems. Different fault scenarios are considered to verify the diagnosis performances, and the satisfactory performances of the proposed diagnosis scheme are validated by the good simulation results. Some interesting future research work is also discussed

On-line estimation approaches to fault-tolerant control of uncertain systems

This thesis is concerned with fault estimation in Fault-Tolerant Control (FTC) and as such involves the joint problem of on-line estimation within an adaptive control system. The faults that are considered are significant uncertainties affecting the control variables of the process and their estimates are used in an adaptive control compensation mechanism. The approach taken involves the active FTC, as the faults can be considered as uncertainties affecting the control system. The engineering (application domain) challenges that are addressed are: (1) On-line model-based fault estimation and compensation as an FTC problem, for systems with large but bounded fault magnitudes and for which the faults can be considered as a special form of dynamic uncertainty. (2) Fault-tolerance in the distributed control of uncertain inter-connected systems The thesis also describes how challenge (1) can be used in the distributed control problem of challenge (2). The basic principle adopted throughout the work is that the controller has two components, one involving the nominal control action and the second acting as an adaptive compensation for significant uncertainties and fault effects. The fault effects are a form of uncertainty which is considered too large for the application of passive FTC methods. The thesis considers several approaches to robust control and estimation: augmented state observer (ASO); sliding mode control (SMC); sliding mode fault estimation via Sliding Mode Observer (SMO); linear parameter-varying (LPV) control; two-level distributed control with learning coordination

Fault estimation and accommodation for virtual sensor bias fault in image-based visual servoing using particle filter

This study develops a fault estimation and accommodation scheme for the image-based visual servoing (IBVS) system to eliminate the effects of the faults due to the image feature extraction task, which is named as bias virtual sensor fault. First, a bias virtual sensor fault in visual servoing is declared. Then, fault diagnosis (FD), which includes fault detection, isolation and estimation, is designed based on the means of particle filter (PF). Finally, a fault accommodation law is developed based on the information obtained from the fault estimation to compensate for the effects of the fault in the system. The proposed fault estimation and accommodation is verified through simulation and experimental studies, and the results show that the system can estimate and eliminate the unknown fault effects effectively

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

Observer-based robust fault estimation for fault-tolerant control

A control system is fault-tolerant if it possesses the capability of optimizing the system stability and admissible performance subject to bounded faults, complexity and modeling uncertainty. Based on this definition this thesis is concerned with the theoretical developments of the combination of robust fault estimation (FE) and robust active fault tolerant control (AFTC) for systems with both faults and uncertainties.This thesis develops robust strategies for AFTC involving a joint problem of on-line robust FE and robust adaptive control. The disturbances and modeling uncertainty affect the FE and FTC performance. Hence, the proposed robust observer-based fault estimator schemes are combined with several control methods to achieve the desired system performance and robust active fault tolerance. The controller approaches involve concepts of output feedback control, adaptive control, robust observer-based state feedback control. A new robust FE method has been developed initially to take into account the joint effect of both fault and disturbance signals, thereby rejecting the disturbances and enhancing the accuracy of the fault estimation. This is then extended to encompass the robustness with respect to modeling uncertainty.As an extension to the robust FE and FTC scheme a further development is made for direct application to smooth non-linear systems via the use of linear parameter-varying systems (LPV) modeling.The main contributions of the research are thus:- The development of a robust observer-based FE method and integration design for the FE and AFTC systems with the bounded time derivative fault magnitudes, providing the solution based on linear matrix inequality (LMI) methodology. A stability proof for the integrated design of the robust FE within the FTC system.- An improvement is given to the proposed robust observer-based FE method and integrated design for FE and AFTC systems under the existence of different disturbance structures.- New guidance for the choice of learning rate of the robust FE algorithm.- Some improvement compared with the recent literature by considering the FTC problem in a more general way, for example by using LPV modeling

Aircraft Abnormal Conditions Detection, Identification, and Evaluation Using Innate and Adaptive Immune Systems Interaction

Abnormal flight conditions play a major role in aircraft accidents frequently causing loss of control. To ensure aircraft operation safety in all situations, intelligent system monitoring and adaptation must rely on accurately detecting the presence of abnormal conditions as soon as they take place, identifying their root cause(s), estimating their nature and severity, and predicting their impact on the flight envelope.;Due to the complexity and multidimensionality of the aircraft system under abnormal conditions, these requirements are extremely difficult to satisfy using existing analytical and/or statistical approaches. Moreover, current methodologies have addressed only isolated classes of abnormal conditions and a reduced number of aircraft dynamic parameters within a limited region of the flight envelope.;This research effort aims at developing an integrated and comprehensive framework for the aircraft abnormal conditions detection, identification, and evaluation based on the artificial immune systems paradigm, which has the capability to address the complexity and multidimensionality issues related to aircraft systems.;Within the proposed framework, a novel algorithm was developed for the abnormal conditions detection problem and extended to the abnormal conditions identification and evaluation. The algorithm and its extensions were inspired from the functionality of the biological dendritic cells (an important part of the innate immune system) and their interaction with the different components of the adaptive immune system. Immunity-based methodologies for re-assessing the flight envelope at post-failure and predicting the impact of the abnormal conditions on the performance and handling qualities are also proposed and investigated in this study.;The generality of the approach makes it applicable to any system. Data for artificial immune system development were collected from flight tests of a supersonic research aircraft within a motion-based flight simulator. The abnormal conditions considered in this work include locked actuators (stabilator, aileron, rudder, and throttle), structural damage of the wing, horizontal tail, and vertical tail, malfunctioning sensors, and reduced engine effectiveness. The results of applying the proposed approach to this wide range of abnormal conditions show its high capability in detecting the abnormal conditions with zero false alarms and very high detection rates, correctly identifying the failed subsystem and evaluating the type and severity of the failure. The results also reveal that the post-failure flight envelope can be reasonably predicted within this framework

Integrated fault estimation and fault-tolerant control for uncertain Lipschitz nonlinear systems

This paper proposes an integrated fault estimation and fault-tolerant control (FTC) design for Lipschitz non-linear systems subject to uncertainty, disturbance, and actuator/sensor faults. A non-linear unknown input observer without rank requirement is developed to estimate the system state and fault simultaneously, and based on these estimates an adaptive sliding mode FTC system is constructed. The observer and controller gains are obtained together via H∞ optimization with a single-step linear matrix inequality (LMI) formulation so as to achieve overall optimal FTC system design. A single-link manipulator example is given to illustrate the effectiveness of the proposed approach

Fault tolerant control for nonlinear aircraft based on feedback linearization

The thesis concerns the fault tolerant flight control (FTFC) problem for nonlinear aircraft by making use of analytical redundancy. Considering initially fault-free flight, the feedback linearization theory plays an important role to provide a baseline control approach for de-coupling and stabilizing a non-linear statically unstable aircraft system. Then several reconfigurable control strategies are studied to provide further robust control performance:- A neural network (NN)-based adaption mechanism is used to develop reconfigurable FTFC performance through the combination of a concurrent updated learninglaw. - The combined feedback linearization and NN adaptor FTFC system is further improved through the use of a sliding mode control (SMC) strategy to enhance the convergence of the NN learning adaptor. - An approach to simultaneous estimation of both state and fault signals is incorporated within an active FTFC system.The faults acting independently on the three primary actuators of the nonlinear aircraft are compensated in the control system.The theoretical ideas developed in the thesis have been applied to the nonlinear Machan Unmanned Aerial Vehicle (UAV) system. The simulation results obtained from a tracking control system demonstrate the improved fault tolerant performance for all the presented control schemes, validated under various faults and disturbance scenarios.A Boeing 747 nonlinear benchmark model, developed within the framework of the GARTEUR FM-AG 16 project “fault tolerant flight control systems”,is used for the purpose of further simulation study and testing of the FTFC scheme developed by making the combined use of concurrent learning NN and SMC theory. The simulation results under the given fault scenario show a promising reconfiguration performance