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The paper aims at developing a novel algorithm to estimate high-order derivatives of rotorcraft angular rates to break the contradiction between bandwidth and filtering performance because high-order derivatives of angular rates are crucial to rotorcraft control. Traditional causal estimation algorithms such as digital differential filtering or various tracking differentiators cannot balance phase-lead angle loss and high-frequency attenuation performance of the estimated differentials under the circumstance of strong vibration from the rotor system and the rather low update rate of angular rates.

The algorithm, capable of estimating angular rate derivatives to maximal second order, fuses multiple attitude signal sources through a first-proposed randomized angular motion maneuvering model independent of platform dynamics with observations generated by cascaded tracking differentiators.

The maneuvering flight test on 5-kg-level helicopter and the ferry flight test on 230-kg-level helicopter prove such algorithm is feasible to generate higher signal to noise ratio derivative estimation of angular rates than traditional differentiators in regular flight states with enough bandwidth for flight control.

The decrease of update rate of input attitude signals will weaken the bandwidth performance of the algorithm and higher sampling rate setting is recommended.

Rotorcraft flight control researchers and engineers would benefit from the estimation method when implementing flight control laws requiring angular rate derivatives.

A purely kinematic randomized angular motion model for flight vehicle is first established, combining rigid-body Euler kinematics. Such fusion algorithm with observations generated by cascaded tracking differentiators to estimate angular rate derivatives is first proposed, realized and flight tested.

This paper aims to focus on the sensor fault-tolerant control (FTC) for civil aircraft under exterior disturbance.

First, a three-step cubature Kalman filter (TSCKF) is designed to detect and isolate the sensor fault and to reconstruct the sensor signal. Meanwhile, a nonlinear disturbance observer (NDO) is designed for disturbance estimation. The NDO and the TSCKF are combined together and an NDO-TSCKF is proposed to solve the problem of sensor faults and bounded disturbances simultaneously. Furthermore, an FTC scheme is designed based on the nonlinear dynamic inversion (NDI) and the NDO-TSCKF.

The method is verified by a Cessna 172 aircraft model under bias gyro fault and constant angular rate disturbance. The proposed NDO-TSCKF has the ability of signal reconstruction and disturbance estimation. The proposed FTC scheme is also able to solve the sensor fault and disturbance simultaneously.

NDO-TSCKF is the novel algorithm used in sensor signal reconstruction for aircraft. Then, disturbance observer-based FTC can improve the flight control system performances when the system with faults.

The NDO-TSCKF-based FTC scheme can be used to solve the sensor fault and exterior disturbance in flight control. For example, the bias gyro fault with constant angular rate disturbance of a civil aircraft is studied.

Signal reconstruction for critical sensor faults and disturbance observer-based FTC for civil aircraft are useful in modern civil aircraft design and development.

This is the research paper studies on the signal reconstruction and FTC scheme for civil aircraft. The proposed NDO-TSCKF is better than the current reconstruction filter because the failed sensor signal can be reconstructed under disturbances. This control scheme has a better fault-tolerant capability for sensor faults and bounded disturbances than using regular NDI control.

The purpose of this paper is to present the development of a Matlab/Simulink‐based simulation environment for the design and testing of indirect and direct adaptive flight control laws with fault tolerant capabilities to deal with the occurrence of actuator and sensor failures.

The simulation environment features a modular architecture and a detailed graphical user interface for simulation scenario set‐up. Indirect adaptive flight control laws are implemented based on an optimal control design and frequency domain‐based online parameter estimation. Direct adaptive flight control laws consist of non‐linear dynamic inversion performed at a reference nominal flight condition augmented with artificial neural networks (NNs) to compensate for inversion errors and abnormal flight conditions following the occurrence of actuator or sensor failures. Failure detection, identification, and accommodation schemes relying on neural estimators are developed and implemented.

The simulation environment provides a valuable platform for the evaluation and validation of fault‐tolerant flight control laws.

The modularity of the simulation package allows rapid reconfiguration of control laws, aircraft model, and detection schemes. This flexibility allows the investigation of various design issues such as: the selection of control laws architecture (including the type of the neural augmentation), the tuning of NN parameters, the selection of parameter identification techniques, the effects of anti‐control saturation techniques, the selection and the tuning of the control allocation scheme, as well as the selection and tuning of the failure detection and identification schemes.

The novelty of this research efforts resides in the development and the integration of a comprehensive simulation environment allowing a very detailed validation of a number of control laws for the purpose of verifying the performance of actuator and sensor failure detection, identification, and accommodation schemes.

The purpose of this paper is to present the irregular deviation examination of flight control surfaces and the potential problem diagnosis of irregular deviations for the jet transport aircraft. A four-jet transport aircraft at transonic flight in cruise phase is the study case of the present article.

The standard lift-to-drag ratio (L/D) and flight dynamic models are established through flight data mining and the fuzzy logic modeling technique based on the flight data of quick access recorder available in the Flight Operations Quality Assurance (FOQA) program of the airlines. The irregular deviations of flight control surfaces are examined by the standard L/D model-predicted results through sensitivity analysis. The contribution values in L/D deficiency are predicted by the deviations and the L/D derivatives of all influencing variables in Taylor series expansion. The potential problems due to irregular deviations can be excavated by the flight dynamic models through the analysis of in-flight stability and controllability.

The magnitude of stabilizer angle to the deficiency of L/D is the largest among the four control surfaces and elevator is the second one through the judgment of contribution values in L/D deficiency. The stabilizer has irregular deviations with obvious endplay problems of jackscrew, as found in the present study. The stabilizer is suggested to have the unscheduled maintenance for the flight control rigging.

The specific transport aircraft of the standard L/D model should be the best one in L/D performance among all transport aircraft in the fleet of the airlines. The present method is a new concept to monitor the irregular deviation of flight control surface. The study case of the four-jet transport aircraft at transonic flight in cruise phase is illustrated as the standard L/D mode. The required flight data of monitored flight is requested to eliminate the biases through compatibility checks. The flight data of study case in the present study is also illustrated as monitored flight data.

To diagnose the irregular deviations of flight control surface deflected angles with contributing to the L/D deficiency estimation is an innovation to improve the flight data analysis of FOQA program for airlines. If the irregular deviation problems of control surfaces can be fixed after rigging in maintenance, the goal of flight safety and aviation fuel saving will be achieved.

The flight control surface rigging of unscheduled maintenance is not expected to coincide with an airline’s peak season or unavailable space in hangar. The optimal time of unscheduled maintenance for the flight control rigging will be easily decided through the correlations between excessive fuel cost and flight safety.

This method can be used to assist airlines to monitor irregular angular positions of flight control surfaces as a complementary tool for management to improve aviation safety, operation and operational efficiency.

The purpose of this paper is to evaluate the accuracy of linear and quasi-steady aerodynamic models of aircraft aerodynamic models when a small unmanned aerial system flies in the presence of strong wind and gust at a high angle of attack and a high sideslip angle.

Compatibility analysis were done to improve the quality of recorded flight test data. A robust method called fuzzy logic modeling is used to set up the aerodynamic models. The reduced frequency is used to represent the unsteadiness of the flow field according to Theodorsen’s theory. The work done by the aerodynamic moments on the motions is used as the criteria of stability.

In portions of flight, aircraft’s stability and control derivatives were unstable and nonlinear functions of airflow angles and angular rates. The roll angle had an important effect on unsteadiness of directional oscillatory damping derivatives. The pilot-induced oscillation and wing rock possibilities were investigated and dismissed so that the lateral directional oscillatory motion was classified as a nonlinear Dutch roll oscillation. Major modeling enhancements or real-time parameter identification are required for the control of a small unmanned aerial system in off-nominal conditions. The robustness tests of all-weather autopilot systems must be done with consideration of sign change.

Oscillatory damping derivatives were reconstructed using flight test data and the inadequacy of engineering level software in predicting this type of instability observed and demonstrated for a flight in the presence of wind shear and external disturbances.

The purpose of this paper is to propose an attitude determination and control scheme for a low‐cost Micro‐satellite with defective inertia. Restricted by the payload design, the z‐axis inertia of this satellite is larger than the x and y axes, which is unstable for natural attitude dynamics.

An original operation mode is designed to avoid z axis from long‐time pointing to the sun during damping, which avoids some unexpected damage. In attitude determination design, EKF and UKF algorithms are compared on estimation accuracy, convergence time and computation complexity in attitude estimation design, which is referred to determine the final estimation scheme. A DSP‐based hardware solution is achieved and a semi‐physical testing and simulation system is built.

Simulation results show the 3‐axis stable mode can be built with the proposed scheme, and the unprotected facet of the satellite can be kept away from long‐time pointing to the sun.

The proposed ADCS scheme can be a reference for the future Micro‐satellite programs which share the similar configuration.

The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm.

Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives.

Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data.

The proposed method requires iterative calculation and can only identify parameters offline.

The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems.

In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

The purpose of this paper is to present the methodology that was used to perform system identification of a dynamically unstable tilt-rotor from flight test data. The method incorporated wavelet transform into the maximum likelihood principle formulation, emphasizing both time and frequency responses. Using wavelets allowed to additionally filter noise in the data, and this increased the estimation quality. This approach did not require measurement and process noise modeling in contrast to the Kalman filter usage for parameter estimation.

In the study, lateral-directional stability and control derivatives of an unstable tiltrotor in hover were estimated. This was performed by applying the maximum likelihood output error method. The estimated model response was decomposed using the Mallat pyramid and matched to wavelet coefficients obtained directly from measurements. In addition, a coherence-based weighting function was used to put more emphasis on the most reliable data. For comparison, the same set of data was used to identify a model with the same structure using the maximum likelihood principle with an incorporated Kalman filter.

It was found that maximum likelihood principle and wavelet transform allowed for estimating aerodynamic coefficients of a dynamically unstable aircraft. The estimation was performed with high accuracy.

The designed method can be used for system identification of unstable aircraft and when additional noise is present (e.g. when noise due to turbulence was observable during the flight test or higher noise levels were present in the sensors data).

The paper presents verification of a wavelet-based maximum likelihood principle output error method using flight test data.

The purpose of this paper is to estimate aircraft roll angle and rate gyro biases using aircraft kinematics with GPS and low‐quality rate gyros.

The proposed method is motivated by observing the fact that, in typical flight applications with transport, reconnaissance, or surveillance missions, the aircraft flies along relaxed flight paths, and the associated aircraft motion can be considered as the sum of the coordinated flight, low‐frequency motion and non‐coordinated, high‐frequency motion. The proposed scheme utilizes the coordinated flight kinematics to form the relatively simple, low‐order Kalman filter that estimates the aircraft roll attitude and rate gyro biases.

The associated frequency analysis reveals that, for the estimation in the relatively low‐frequency region, the method relies primarily on GPS with the help of coordinated flight kinematics in removing the bias effect from the low‐quality rate gyros. Also, for the estimation in the high‐frequency region the method relies mainly on the numerical integration of the rate gyro for the roll attitude, which enables a high‐bandwidth estimation.

The proposed method is not suitable for flight manoeuvres where a non‐coordinated flight, such as steady heading sideslip, is sustained for a long period of time.

The proposed method has been implemented in a small UAV. The associated flight tests and simulations indicate that the new method has a potential to be used as a backup or a replacement for other complex conventional methods for many flight applications.

This paper has been the first to promote the estimation method that combines aircraft kinematics with GPS and low‐quality rate gyros.

The magnetometer measurement update plays a key role in correcting yaw estimation in fusion algorithms, and hence, the yaw estimation is vulnerable to magnetic disturbances. The purpose of this study is to improve the ability of the fusion algorithm to deal with magnetic disturbances.

In this paper, an adaptive measurement equation based on vehicle status is derived, which can constrain the yaw estimation from drifting when vehicle is running straight. Using this new measurement, a Kalman filter-based fusion algorithm is constructed, and its performance is evaluated experimentally.

The experiments results demonstrate that the new measurement update works as an effective supplement to the magnetometer measurement update in the present of magnetic disturbances, and the proposed fusion algorithm has better yaw estimation accuracy than the conventional algorithm.

The paper proposes a new adaptive measurement equation for yaw estimation based on vehicle status. And, using this measurement, the fusion algorithm can not only reduce the weight of disturbed sensor measurement but also utilize the character of vehicle running to deal with magnetic disturbances. This strategy can also be used in other orientation estimation fields.