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The purpose of this paper is to generate residuals which can be used to detect fault and isolate on a vertical takeoff and landing (VTOL) aircraft dynamic model.

In the proposed approach, a generalized observer scheme method based on an unknown input observer is used for residual generation and applied to detect and isolate a faulty sensor. A bank of robust unknown input observers estimates the state variables of the system and gathers necessary information for fault detection and isolation purposes.

A sinus signal is considered as a non‐linear disturbance in simulations. A failure simulation was prepared in different times. In this situation an unknown input observer should be designed which could predict the states of the system against the disturbances or unknown inputs. In the real world, there exist unknown inputs such as system non‐linearities, noise and disturbances. The paper shows that the system based on UIO is robust for unknown inputs mentioned above.

It is simulated on a VTOL dynamic model using MATLAB/Simulink. Any single sensor fault could be detected and isolated correctly. This kind of observer is also robust and flexible.

In order to improve the practicability of the design in state estimation, the paper aims to present a novel disturbance decoupled reduced‐order observer (DDRO) design scheme.

The paper first uses equivalence transformation to eliminate unknown input appearing in measurement. Then based on Luenberger observer and using two non‐singular coordinate transformation, the design observer can get no bias error in the state estimation.

By using this approach we find that the conditions of DDRO depend on the system itself that is weaker than other observers. It is a qualified and simple and straightforward approach to be applied in wide domains.

We should note that the number of independent rows of the matrix C must not be less than the number of the independent columns of the matrix E to satisfy condition rank(CE)=rank(E)=q. In other words, the maximum number of disturbances which can be decoupled cannot be larger than the number of independent measurements.

It is a very useful approach to solve the problem that the measurement is contaminated by disturbances.

The paper proposed an equivalence transformation. It is used to eliminate unknown input appearing in measurement. At the same time the algebraic transformation guaranteed that it would lose no information of the unknown inputs. And compared with other known results, the design condition of the reduced‐order observer which proposed in this paper depends on system itself, especially, which is weaker than others.

This paper aims to present a novel fault estimator design scheme, in order to improve the practicability of the fault estimation.

The paper first transforms the system state into three parts. Then a reduced‐observer is designed for an unknown input and fault‐free system, and the observer can get no bias error in the state estimation. So the estimator can get the exact estimation for the unknown input and fault of the actuator.

By using this approach it is found that the condition of the estimator is weaker than other observers. It is a qualified and simple and straightforward approach to be applied in wide domains.

It should be noted that the sensors should be perfectly reliable. The maximum number of disturbances which can be decoupled cannot be larger than the number of independent actuators.

It is a very useful approach to solve the problem that the actuator is contaminated by disturbances.

The paper uses a transformation to divide the state into three parts. By estimating the part that is not affected by the disturbance and fault, it gets the exact estimation for the unknown input and fault of the actuator. The design condition of the reduced‐order observer which is proposed is weaker than others.

The purpose of this study is to present a new integrated structure for a fault tolerant aircraft control system because fault diagnosis of flight control systems is extremely important in obtaining healthy flight. An approach to detect and isolate aircraft sensor faults is proposed, and a new integrated structure for a fault tolerant aircraft control system is presented.

As disturbance and sensor faults are mixed together in a flight control system, it is difficult to isolate any fault from the disturbance. This paper proposes a robust unknown input observer for state estimation and fault detection as well as isolation using fuzzy logic.

The dedicated observer scheme (DOS) and generalized observer scheme (GOS) are used for fault detection and isolation in an observer-based approach. Using the DOS, it has been shown through simulation that sensor fault detection and isolation can be made, but here the threshold value must be well chosen; if not, the faulty sensor cannot be correctly isolated. On the other hand, the GOS is more usable and flexible than the DOS and allows isolation of faults more correctly and for a fuzzy logic-based controller to be used to realize fault isolation completely.

The fuzzy logic approach applied to the flight control system adds an important key for sensor fault isolation because it reduces the effect of false alarms and allows the identification of different kinds of sensor faults. The proposed approach can be used for similar systems.

This paper proposes a novel method of fault detection, which is based on H_/H∞ Runge–Kutta observer and an adaptive threshold for a class of closed-loop non-linear systems. The purpose of this paper is to improve the rapidity and accuracy of fault detection.

First, the authors design the H_/H∞ Runge–Kutta fault detection observer, which is used as a residual generator to decouple the residual from the input. The H_ performance index metric in the specified frequency domain is used to describe how sensitive the residual to the fault. The H∞ norm is used to describe the residual robustness to the external disturbance of the systems. The residual generator is designed to achieve the best tradeoff between robustness against unknown disturbances but sensitivity to faults, thus realizing the accurate detection of the fault by suppressing the influence of noise and disturbance on the residual. Next, the design of the H_/H∞ fault detection observer is transformed into a convex optimization problem and solved by linear matrix inequality. Then, a new adaptive threshold is designed to improve the accuracy of fault detection.

The effectiveness and correctness of the method are tested in simulation experiments.

This paper presents a novel approach to improve the accuracy and rapidity of fault detection for closed-loop non-linear system with disturbances and noise.

Condition monitoring and health management of an aircraft engine is of importance due to engine’s critical position in aircraft. Missions require uninterrupted and safer conditions during the flight or taxi operations. Hence, the deviations, abnormal situations or failures have to be under control. This paper aims to propose a cascade connected approach for an aircraft engine fault tolerant control.

The cascade connected structure includes a full-order unknown input observer for fault detection and eliminating the unknown disturbance effect on system, a generalized observer scheme for fault isolation and a Boolean logic mechanism for decision-making in reconfiguration process, respectively. This combination is simulated on a linear turbojet engine model in case of unknown input disturbance and under various sensor failure scenarios.

The simulation results show that the suggested fault detection isolation reconfiguration (FDIR) approach works effectively for multiple sensor failures with various amplitudes.

Different from other studies, the proposed model is sensitive to unknown input disturbance and failures that have unknown amplitudes. One another notable feature of suggested FDIR approach is adaptability of structure against multiple sensor failures. Here, it is assumed that only a single fault is to be detected and isolated at a time. The simulation results show that the proposed structure can be suggested for linear models especially for physical redundancy-based real-time applications easily, quickly and effectively.

Increasing commercial applications for small unmanned aircraft create growing challenges in providing safe flight conditions. The conventional measures to detect icing are either expensive, energy consuming or heavy. The purpose of this paper is to develop a fault identification and isolation scheme using unknown input observers to detect and isolate actuator and structural faults in simultaneous occurrence.

The fault detection scheme is based on a deviation in system parameters due to icing and lock-in-place (LIP), two faults from different categories with similar indications that require different reconfiguration actions. The obtained residual signals are selected to be triggered by desired faults, while insensitive to others.

The proposed observer is sensitive to both actuator and structural faults, and distinguishes simultaneous occurrences by insensitivity to LIP in selected residue signals. Simulation results confirm the success of the proposed system in the presence of uncertainty and disturbance.

The fault detection and isolation scheme proposed here is based on the linear model of a winged aircraft, the Aerosonde. Moreover, the faults are applied to rudder and aileron in simulations, but the design procedure for other models is provided. The designed scheme could be further implemented on a non-linear aircraft model.

Applying the proposed icing detection scheme increases detection system reliability, since fault isolation enables timely reconfiguration schemes.

The observers proposed in previous papers detected icing fault but were not insensitive to actuator faults.

The purpose of this paper is to propose an adaptive neural-sliding mode-based observer for the estimation and reconstruction of unknown faults and disturbances for time-varying nonlinear systems such as aircraft, to ensure preciseness in the diagnosis of fault magnitude as well as the shape without enhancement of system complexity and cost. Fault-tolerant control (FTC) strategy based on adaptive neural-sliding mode is also proposed in the existence of faults for ensuring the stability of the faulty system.

In this paper, three strategies are presented: adaptive radial basis functions neural network (ARBFNN), conventional radial basis functions neural network (CRBFNN) and integral-chain differentiator. For the purpose of enhancement of fault diagnosis and isolation, a new sliding mode-based concept is introduced for the weight updating parameters of radial basis functions neural network (RBFNN).The main objective of updating the weight parameters adaptively is to enhance the effectiveness of fault diagnosis and isolation without increasing the computational complexities of the system. Results depict the effectiveness of the proposed ARBFNN approach in fault detection (FD) and approximation compared to CRBFNN, integral-chain differentiator and schemes existing in literature. In the second step, the FTC strategy is presented separately for each observer in the presence of unknown faults and failures for ensuring the stability of the system, which is validated on Boeing 747 100/200 aircraft.

The proposed adaptive neural-sliding mode approach is investigated, which depicts more effectiveness in numerous situations such as faults, disturbances and uncertainties compared to algorithms used in literature. In this paper, both the fault approximation and isolation and the fault tolerance approaches are studied.

For the enhancement of safety level as well as for avoiding any kind of damage, timely FD and fault tolerance have always had a significant role; therefore, the algorithms proposed in this research ensure the tolerance of faults and failures, which plays a vital role in practical life for avoiding any kind of damage.

In this study, a new neural-sliding mode concept is adopted for the adaptive faults approximation and reconstruction, and then the FTC algorithms are studied for each observer separately, whereas in previous studies, only the fault detection and isolation (FDI) or the fault tolerance problems were studied. Results demonstrate the effectiveness of the proposed strategy compared to the approaches given in the literature.

The purpose of this paper is to present an actuator fault detection method for unmanned aerial vehicles (UAVs) based on interval observer and extended state observer.

The proposed algorithm has very little model dependency. Therefore, a six-degree-of-freedom linear equation of UAVs is first established, and then, combined with actuator failure and external disturbances in flight control, a steering gear model with actuator failure (such as stuck bias and invalidation) is designed. Meanwhile, an extended state observer is designed for fault detection. Moreover, a fault detection scheme based on interval observer is designed by combining fault and disturbances.

The method is testified on the extended state observer and the interval observer under the failure of the steering gear and bounded disturbances. The simulation results show that the two types of fault detection schemes designed can successfully detect various types of faults and have high sensitivity.

This research paper studies the failure detection scheme of the UAVs’ actuator. The fault detection scheme in this paper has better performance on actuator faults and bounded disturbances than using regular fault detection schemes.

Fault detection, isolation and reconfiguration of the flight control system is an important problem to obtain healthy flight. This paper aims to propose an integrated approach for aircraft fault-tolerant control.

The integrated structure includes a Kalman filter to obtain without noise, a full order observer for sensor fault detection, a GOS (generalized observer scheme) for sensor fault isolation and a fuzzy controller to reconfigure of the healthy sensor. This combination is simulated using the state space model of a lateral flight control system in case of disturbance and under sensor fault scenario.

Using a dedicated observer scheme, the detection and time of sensor fault are correct, but the sensor fault isolation is evaluated incorrectly while the faulty sensor is isolated correctly using GOS. The simulation results show that the suggested approach works affectively for sensor faults with disturbance.

This paper proposes an integrated approach for aircraft fault-tolerant control. Under this framework, three units are designed, one is Kalman filter for filtering and the other is GOS for sensor fault isolation and another is fuzzy logic for reconfiguration. An integrated approach is sensitive to faults that have disturbances. The simulation results show the proposed integrated approach can be used for any linear system.