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The purpose of this paper is to propose a new approach for aerodynamic parameter identification of hypersonic vehicles, which is based on Pigeon-inspired optimization (PIO) algorithm, with the objective of overcoming the disadvantages of traditional methods based on gradient such as New Raphson method, especially in noisy environment.

The model of hypersonic vehicles and PIO algorithm is established for aerodynamic parameter identification. Using the idea, identification problem will be converted into the optimization problem.

A new swarm optimization method, PIO algorithm is applied in this identification process. Experimental results demonstrated the robustness and effectiveness of the proposed method: it can guarantee accurate identification results in noisy environment without fussy calculation of sensitivity.

The new method developed in this paper can be easily applied to solve complex optimization problems when some traditional method is failed, and can afford the accurate hypersonic parameter for control rate design of hypersonic vehicles.

In this paper, the authors converted this identification problem into the optimization problem using the new swarm optimization method – PIO. This new approach is proved to be reasonable through simulation.

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 study is to estimate aerodynamic parameters using regularized regression-based methods.

Regularized regression methods used are LASSO, ridge and elastic net.

A viable option of aerodynamic parameter estimation from regularized regression-based methods is found.

Efficacy of the methods is examined on flight test data.

This study provides regularized regression-based methods for aerodynamic parameter estimation from the flight test data.

The main purpose of this work was elaboration and verification of a method of assessing the sensitivity of automatic control laws to parametric uncertainty of an airplane’s mathematical model. The linear quadratic regulator (LQR) methodology was used as an example design procedure for the automatic control of an emergency manoeuvre. Such a manoeuvre is assumed to be pre-designed for the selected airplane.

The presented method of investigating the control systems’ sensitivity comprises two main phases. The first one consists in computation of the largest variations of gain factors, defined as differences between their nominal values (defined for the assumed model) and the values obtained for the assumed range of parametric uncertainty. The second phase focuses on investigating the impact of the variations of these factors on the behaviour of automatic control in the manoeuvre considered.

The results obtained allow for a robustness assessment of automatic control based on an LQR design. Similar procedures can be used to assess in automatic control arrived at through varying design methods (including methods other than LQR) used to control various manoeuvres in a wide range of flight conditions.

It is expected that the presented methodology will contribute to improvement of automatic flight control quality. Moreover, such methods should reduce the costs of the mathematical nonlinear model of an airplane through determining the necessary accuracy of the model identification process, needed for assuring the assumed control quality.

The presented method allows for the investigation of the impact of the parametric uncertainty of the airplane’s model on the variations of the gain-factors of an automatic flight control system. This also allows for the observation of the effects of such variations on the course of the selected manoeuvre or phase of flight. This might be a useful tool for the design of crucial elements of an automatic flight control system.

The main targets of the work are analysis and simulation of flying laboratory performance. In particular, synthesis of control system for handling qualities change and evaluation in flight are taken into consideration.

Modification of handling qualities is obtained by applying indirect flight control system (FBW). The properties of the optimal controller are calculated through the indirect (implicit) model‐following method. In particular, the modified version based on the computer simulations is used.

Calculation and simulation concern the synthesis of desired handling qualities of the general aviation aircraft PZL‐M20 “Mewa” equipped with indirect (FBW) experimental flight control system. Results of the simulation show that the flying laboratory has the same properties as modeled aircraft, and it is possible to say that handling properties concern attitude orientation of the experimental aircraft is similar to modeled commuter aircraft.

The result of research can be implemented on a project of the flying laboratory based on general aviation aircraft PZL M20 “Mewa”.

The paper presents the practical approach for synthesis of the “Simplified total in flight simulator” performance which can be used for analysis of handling qualities of general aviation aircraft equipped with FBW. Research of this type focuses on military and transport airplanes however, there are no published works in the area of small aircraft so far.

The purpose of his paper is to study the radar stealth performance of a Y-type quadrotor with coaxial rotors and parallel rotors.

This Y-type quadrotor is designed as an aerodynamic layout with parallel twin rotors at the front and coaxial twin rotors at the rear. The multi-rotor scattering (MRS) method based on multi-rotor dynamic simulation (MRDS) and electromagnetic scattering module (ESM) is presented. MRDS is used to simulate the complex rotation of parallel rotors and coaxial rotors. ESM is used to calculate the instantaneous radar cross-section (RCS) of the quadrotor.

For a single rotor, the minimum period of the RCS curve at a given azimuth is equal to the basic passage time of the blade, where increasing the speed can shorten this minimum period. When the elevation angle increases, the forward RCS fluctuation of the quadrotor increases, while the average RCS decreases. The change of the roll angle will affect both the mean and the maximum difference of the RCS–time curve at the given lateral azimuth. The increase of the pitch angle will enhance the dynamic amplitude of the RCS–time curve under the forward azimuth.

The research in this article can provide reference for the stealth design of the Y-type quadcopter in the future.

The originality is the establishment of the MRS method. This method could provide value for dealing with the electromagnetic scattering problem of coaxial rotors and parallel rotors.

The purpose of this paper is to investigate the estimation methodology with a highly generalized cost-effective single hidden layer neural network.

The aerodynamic parameter estimation is a challenging research area of aircraft system identification, which finds various applications such as flight control law design and flight simulators. With the availability of the large database, the data-driven methods have gained attention, which is primarily based on the nonlinear function approximation using artificial neural networks. A novel single hidden layer feed-forward neural network (FFNN) known as extreme learning machine (ELM), which overcomes the issues such as learning rate, number of epochs, local minima, generalization performance and computational cost, as encountered in the conventional gradient learning-based FFNN has been used for the nonlinear modeling of the aerodynamic forces and moments. A mathematical formulation based on the partial differentiation is proposed to estimate the aerodynamic parameters.

The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters using the proposed methodology. The efficacy of the estimates is verified with the results obtained through the conventional parameter estimation methods such as the equation-error method and filter-error method.

The present study is an outcome of the research conducted on ELM for the estimation of aerodynamic parameters from the real flight data. The proposed method is capable to estimate the parameters in the presence of noise.

The purpose of this paper is to build a neural model of an aircraft from flight data and online estimation of the aerodynamic derivatives from established neural model.

A neural model capable of predicting generalized force and moment coefficients of an aircraft using measured motion and control variable is used to extract aerodynamic derivatives. The use of neural partial differentiation (NPD) method to the multi-input-multi-output (MIMO) aircraft system for the online estimation of aerodynamic parameters from flight data is extended.

The estimation of aerodynamic derivatives of rigid and flexible aircrafts is treated separately. In the case of rigid aircraft, longitudinal and lateral-directional derivatives are estimated from flight data. Whereas simulated data are used for a flexible aircraft in the absence of its flight data. The unknown frequencies of structural modes of flexible aircraft are also identified as part of estimation problem in addition to the stability and control derivatives. The estimated results are compared with the parameter estimates obtained from output error method. The validity of estimates has been checked by the model validation method, wherein the estimated model response is matched with the flight data that are not used for estimating the derivatives.

Compared to the Delta and Zero methods of neural networks for parameter estimation, the NPD method has an additional advantage of providing the direct theoretical insight into the statistical information (standard deviation and relative standard deviation) of estimates from noisy data. The NPD method does not require the initial value of estimates, but it requires a priori information about the model structure of aircraft dynamics to extract the flight stability and control parameters. In the case of aircraft with a high degree of flexibility, aircraft dynamics may contain many parameters that are required to be estimated. Thus, NPD seems to be a more appropriate method for the flexible aircraft parameter estimation, as it has potential to estimate most of the parameters without having the issue of convergence.

This paper highlights the application of NPD for MIMO aircraft system; previously it was used only for multi-input and single-output system for extraction of parameters. The neural modeling and application of NPD approach to the MIMO aircraft system facilitate to the design of neural network-based adaptive flight control system. Some interesting results of parameter estimation of flexible aircraft are also presented from established neural model using simulated data as a novelty. This gives more value addition to analyzing the flight data of flexible aircraft as it is a challenging problem in parameter estimation of flexible aircraft.

The purpose of this paper is to identify the flexible aircraft model accurately from the frequency responses.

The frequency domain output error method is used to estimate the aerodynamic (rigid body and elastic body) derivatives, and mode shape parameters in the process of identification of flexible aircraft model. The accurate identification of lightly damped low frequency rigid-body response modes requires a careful selection of the frequency sweep length and the fast Fourier transform (FFT) window size, as the FFT window length cannot be longer than any individual sweep records. To address this issue, an effort is made to derive the FFT window length for the application of frequency domain estimation approach.

The investigations are initially made to select a suitable FFT window size for the accurate identification of the lightly damped low frequency rigid-body response modes of the flexible aircraft. Subsequently, frequency domain estimation approach is applied to simulated data of flexible aircraft. Besides the stability and control derivatives, the structural modes of the flexible aircraft are also estimated as part of state space model identification, and it is shown that all the model parameter estimates are accurate. Identification of such flexible aircraft aerodynamic (rigid body and elastic body) derivatives and structural mode shape parameters will lead to mathematical models of flexible aircraft that are accurate over a wide frequency range. The identified models are validated using the time response of frequency sweep data.

Aircraft system identification is an integral part of aerospace system design and life cycle process. This becomes a complex process when the aircraft has significant effects of flexibility on the flight dynamics, especially as the frequencies of the elastic modes become lower and approach those of the rigid body modes. Thus, an integrated mathematical model of flexible aircraft is required to develop, and it should be valid for a wide frequency range and relevant for the design of flight control system.

This paper focuses on the application of frequency domain approach to identify the valid model of flexible aircraft by estimating the aerodynamic (rigid body and elastic body) derivatives and structural mode shape parameters of flexible aircraft. The unknown frequencies of structural modes are also able to identify accurately in frequency domain. This gives more value addition to analyze the flight data of flexible aircraft, as it is challenging problem in parameter estimation of flexible aircraft.

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.