Aerodynamic Parameter Identification Research Articles

An active and passive combined fault-tolerant control approach for UAV with the control surface fault

In this paper, the fault-tolerant control (FTC) problem of fixed-wing unmanned aerial vehicle (UAV) with control surface structural damage is studied. An active-passive combined FTC algorithm based on sliding mode and adaptive control is proposed. Considering that the UAV is required to be instantaneously stable in the initial stage of the fault, a one-way auxiliary surface sliding mode controller based on fast dual power reaching law is adopted. During this period, the nonlinear recursive least squares method is used to identify the aerodynamic parameters of the UAV after the fault. Then, an adaptive control combined with aerodynamic parameter identification is designed to actively compensate for the fault. In addition, a hardware-in-the-loop simulation platform is developed to verify the active and passive combined FTC method. The simulation results show that the algorithm reduces the control error of the UAV after the fault and improves the robustness and reliability. This method provides a reliable technical guarantee for the safe flight of UAV under the condition of structural damage of control surface and has important practical significance for improving the reliability and safety of UAV.

Identification of Lateral-Directional Aerodynamic Parameters via an Online Correction Wind Tunnel Virtual Flight Test

Identification of Lateral-Directional Aerodynamic Parameters via an Online Correction Wind Tunnel Virtual Flight Test

Aerodynamic Parameter Identification Based on Multi Group Adaptive Search Optimization Algorithm

Abstract Due to the strong coupling and nonlinearity of spinning-stabilized correction projectile’s motion and dynamic model, its aerodynamic parameter identification can be transformed into a high-dimensional multi extremum optimization problem. To avoid the excessive computational complexity and susceptibility to local optima, this paper proposes a multiple swarm adaptive search optimization method. This method automatically divides the samples into several subpopulations through an adaptive density peak clustering method, and then searches and updates in each subpopulation. To balance convergence speed and population diversity, the update operator for each generation combines the random search and optimal convergence step size, whose weights can be adaptively adjusted based on the update rate and dispersion characteristics of the subpopulations. Applying this method to the aerodynamic parameter identification, and comparing with improved particle swarm optimization and genetic algorithm, simulation results show that a better identification result can be obtained at the same iterations.

Physics-Informed Transfer Learning-Based Aerodynamic Parameter Identification of Morphing Aircraft

This paper presents an aerodynamic parameter identification method for morphing aircraft through physics-informed transfer learning. The basic configuration of the morphing aircraft is first identified using physics-informed neural networks (PINN), which identifies aerodynamic parameters by integrating the flight dynamic model as a regularization term within the loss function. When the morphing aircraft transitions to the next configuration, the unidentified aerodynamic parameters change accordingly. To rapidly identify the aerodynamic parameters after deformation, a transfer learning approach is adopted. The advantages of both PINN and transfer learning are combined by this method, and a significant reduction in the required flight data for aerodynamic parameter identification can be achieved along with an improvement in model accuracy. The effectiveness of this method is demonstrated through simulation experiments. In scenarios with limited simulated flight data and noise, superior parameter identification accuracy is achieved by this method.

Adaptive PSO-SO algorithm with Sobol sequence for aerodynamic physical parameter identification of projectiles

In the domain of aerodynamic physical parameter identification, conventional optimization algorithms are often limited by falling into local optima. To overcome this limitation, a novel adaptive PSO-SO algorithm based on Sobol sequences (SAPSO-SO) algorithm is proposed in this study. The algorithm integrates particle swarm optimization algorithms and snake optimization algorithms, utilizing Sobol sequences for initialization, which enhances the global search and local development ability of the algorithm by adaptively adjusting the inertia weights and learning factors. In addition, this study introduced a local optimal discriminant mechanism and a local search function to further enhance the optimization performance of the algorithms. In this study, the small interval constant method was used to subdivide the trajectory, relying on the three-degree-of-freedom ballistic model to identify the starting ballistic parameters and aerodynamic physical parameters of each small interval. The performances of the snake optimization algorithm, particle swarm optimization algorithm, C-K method, and SAPSO-SO algorithm in the identification of ballistic physical parameters were compared using the full ballistic simulation data of a high-speed rotating projectile as measurement data. The results show that the SAPSO-SO algorithm demonstrates excellent accuracy and effectiveness, especially in noisy simulation data, where its recognition accuracy is improved by 7.79% over the C-K method, highlighting its superior anti-noise performance and global optimization capability. It is comprehensively analyzed that the SAPSO-SO algorithm has strong global optimization potential in theory and shows a high degree of accuracy and stability in practical applications, independent of the selection of initial parameters.

Optimizing projectile aerodynamic parameter identification of kernel extreme learning machine based on improved Dung Beetle Optimizer algorithm

Optimizing projectile aerodynamic parameter identification of kernel extreme learning machine based on improved Dung Beetle Optimizer algorithm

Angular acceleration estimation and aerodynamic parameter identification based on angular velocity equivalent model

Conventional angular acceleration estimation methods generate non-negligible errors when the control surface rapidly deflects; hence, the estimated angular acceleration cannot be used for aerodynamic parameter identification directly. This paper proposes an angular acceleration estimation method based on angular velocity equivalent model. The angular acceleration of the angular velocity equivalent model, whose angular velocity response is consistent with the flight test data, is used as the estimated angular acceleration. Firstly, the equivalent angular velocity model is established by combining the generic aerodynamic model with the rotational dynamic equations of aircraft. Secondly, the angular velocity data and the control surface deflection data are synchronized. Finally, the angular velocity response of the equivalent model is used as the predictor, and the estimated angular acceleration is obtained by extended Kalman filter. The aerodynamic parameter identification and validation are carried out using the estimated angular acceleration of a large civil aircraft flight test data. The results show that the angular acceleration obtained through the proposed angular acceleration estimation method meets the accuracy requirement of aerodynamic parameter identification.

Identification of aircraft longitudinal aerodynamic parameters using an online corrective test for wind tunnel virtual flight

Identification of aircraft longitudinal aerodynamic parameters using an online corrective test for wind tunnel virtual flight

Adaptive fault‐tolerant control for aircraft against control surface failure diagnosed by set‐membership estimation

AbstractThis paper focuses on the fault diagnosis method and the fault‐tolerant control method of the aircraft with control surface damage. The fault diagnosis method is based on the set‐membership estimation. To diagnose the fault of control surface under the condition of data containing noise and uncertainty, a multi‐index optimized fault residual observer based on sensitivity and robustness is designed for the observation of aircraft fault information. Based on the observed fault residual information, the set‐membership estimation theory is used to determine whether the aircraft control surface fault occurs. The fault‐tolerant control method is based on dynamic inverse control via aerodynamic parameter identification. Furthermore, to reduce the nonlinear effects of unmodeled dynamics and aerodynamic identification errors after a fault, an adaptive disturbance rejection observer is designed for control compensation. The proposed methods are evaluated by numerical simulation. Results demonstrate that the fault diagnosis method can effectively diagnose under the influence of uncertain noise and the attitude control has the robustness of different faults.

Online Identification of Aerodynamic Parameters of Experimental Rockets Based on Unscented Kalman Filtering

Online identification of aerodynamic parameters of experimental rockets was completed based on unscented Kalman filtering (UKF). Numerical simulation, hardware-in-the-loop (HIL) simulation, and flight tests were conducted. The identification error of aerodynamic force in numerical simulation and HIL simulation is within 2%. For flight test data, trajectory reconstruction was performed using the identified aerodynamic forces, and the results showed that the identification results were more accurate than the interpolation table calculation results. The flight test identification results show that the identification method can complete parameter online identification under the conditions of limited performance of onboard computers, real sensor errors, and servo response. The approximate linear correlation between α and δe and the reason for their formation from the moment balance were analyzed. It was pointed out that when the recognition sampling period is long, this phenomenon will affect the identification of parameters, and a solution is proposed.

Time-Domain Identification Method Based on Data-Driven Intelligent Correction of Aerodynamic Parameters of Fixed-Wing UAV

In order to overcome the influence of complex environmental disturbance factors such as nonlinear time-varying characteristics on the dynamic control performance of small fixed-wing UAVs, the nonlinear expression relationship of neural networks (NNs) is combined with the recursive least squares (RLSs) identification algorithm. This paper proposes a hybrid aerodynamic parameter identification method based on NN-RLS offline network training and online learning correction. The simulation results show that compared with the real value of the identification value obtained by this algorithm, the residual error of the moment coefficient is reduced by 69%, and the residual error of the force coefficient is reduced by 89%. Under the same identification accuracy, the identification time is shortened from the original 0.1 s to 0.01 s. Compared with traditional identification algorithms, better estimation results can be obtained. By using this algorithm to continuously update the NN model and iterate repeatedly, iterative learning for complex dynamic models can be realized, providing support for the optimization of UAV control schemes.

Improved Adaptive NDI Flight Control Law Design Based on Real-Time Aerodynamic Identification in Frequency Domain

The traditional aircraft controller design is usually based on the off-line aerodynamic model. Due to the deviation of the off-line aerodynamic model, the flight quality is difficult to meet the requirements when the aircraft is flying in the real atmosphere. To solve this problem, this paper proposes a frequency domain identification-based improved adaptive nonlinear dynamic inversion (NDI) control method (FDI-ANDI). In this paper, an online recursive aerodynamic parameter identification method in the frequency domain is first designed, and then an adaptive dynamic inversion control method based on the online aerodynamic parameter identification results is established. Finally, aiming at the problem of the slow response speed of the NDI controller, an improved adaptive dynamic inversion control law is designed by using the method of series lead correction. Compared with the traditional control method, the adaptive dynamic inversion method based on online aerodynamic identification has stronger robustness and a faster response speed in the face of model uncertainty. The final simulation analysis shows that the method has a better control effect than the traditional control method.

Identification of aero-physical parameters of projectile based on maximum likelihood estimation algorithm

The physical identification of aerodynamic parameters plays a decisive role in studying the characteristics of projectiles. During the development process of projectiles, it is an important task to accurately obtain the aerodynamic parameters of projectiles. In this paper, through a large number of experiments, the flight trajectory data of the projectile are measured, and the maximum likelihood estimation algorithm is used to physically identify the drag coefficient and lift coefficient of the projectile. First, the sensitivity coefficients of each parameter of the projectile trajectory are calculated and deduced. Second, based on the consistency and asymptotic characteristics of the maximum likelihood estimation algorithm, the sensitivity relationship between the velocity and the drag coefficient and between the position of the projectile’s center of mass and the lift coefficient is used to identify the aerodynamic physical parameters of the projectile. The results show that the maximum likelihood estimation algorithm has high identification accuracy, fast calculation speed, and low algorithm complexity, which can effectively identify the aerodynamic physical parameters of the projectile and meet practical engineering needs.

Identification of Lateral-Directional Aerodynamic Parameters for Aircraft Based on a Wind Tunnel Virtual Flight Test

In the early stages of aircraft design, a scaled model of the aircraft is installed in a wind tunnel for dynamic semi-free flight to approximate real flight, and the test data are then used to identify the aerodynamic parameters. However, the absence of the translational motion of the test model makes its flight dynamics different from those in free flight, and the effect of this difference on parameter identification needs to be investigated. To solve this problem, a 3-DOF wind tunnel virtual flight test device is built to fix the test model on a rotating mechanism, and the model is free to rotate in three axes through the deflection of the control surfaces. The flight dynamics equations for the wind tunnel virtual flight test are established and expressed as a decoupled form of the free flight force and the influence of the test support frame force on the model’s motions through linearization. The differences between wind tunnel virtual flight and free flight are analysed to develop a model for the identification of aerodynamic parameters. The selection of the lateral-directional excitation signal and the design method of its parameters are established based on the requirements for the identifiability of the aerodynamic derivatives, and a step-by-step method for the identification of aerodynamic force and moment derivatives is established. The aerodynamic parameter identification results of a blended wing body aircraft show that the identification method proposed in this paper can obtain results with high accuracy, and the response of the modified motion model is consistent with that of the free flight motion model.

A combined aerodynamic parameter identification method for missing test data

In order to solve the problem that some test data cannot be measured or the measurement is difficult, a combined aerodynamic parameters identification method for missing test data is proposed. In this method, the aerodynamic parameter identification problem is modified into an optimization problem. The initial value of the flight state and the aerodynamic parameter interpolation table are used as design variables, and the motion equation of the aircraft including all aerodynamic parameters is used as a model to construct an objective function containing multiple pieces of test data information. In the optimization, the aerodynamic parameter database and the identification results of the existing methods are used as the prior knowledge. The initial value of the unmeasured data is fitted as the reference value. Then, the feasible sample selection method is designed. Finally, the differential evolution algorithm is used to solve the problem. The proposed method is used to process 264 pieces of test data, and the results show that compared with the existing aerodynamic parameter identification methods, the proposed identification method can obtain all aerodynamic parameters with higher accuracy and can inversely calculate and obtain unmeasured flight test data practical engineering significance.

Flight Dynamics Modeling and Aerodynamic Parameter Identification of Four-Degree-of-Freedom Virtual Flight Test

Compared with the forced oscillation test in a conventional wind tunnel test, the wind tunnel virtual flight test can achieve partially constrained dynamic motion of the model in the wind tunnel by manipulating the control surface deflection. In this paper, a new four-degree-of-freedom (4-DOF) wind tunnel virtual flight test device is established. Compared with the 3-DOF wind tunnel virtual flight test with rotation around the center of mass, the model adds vertical motion, enabling it to simulate free flight more realistically. In this paper, the nonlinear flight dynamics model of 4-DOF wind tunnel virtual flight test is established, the differences in dynamics characteristics between it and free flight are analyzed, and the identification model of aerodynamic derivatives based on 4-DOF wind tunnel virtual flight test is derived. To ensure the safety of the test, the longitudinal altitude control law as well as the sideslip and roll angle control laws in the lateral direction are designed. Finally, a set of aerodynamic parameter identification methods based on a 4-DOF wind tunnel virtual flight test is formed. The output error method is used to identify the aerodynamic derivatives of the test model, and the accuracy of the identification results is higher than that of the 3-DOF wind tunnel virtual flight test.

Identification and Modeling Method of Longitudinal Stall Aerodynamic Parameters of Civil Aircraft Based on Improved Kirchhoff Stall Aerodynamic Model

The stall aerodynamic model based on Kirchhoff’s theory of flow separation is widely used in the identification and modeling of stall aerodynamic parameters. However, it has two defects. First, its model structure is significantly different from the pre-stall model used for a small attack angle, meaning the identification results cannot be combined with the pre-stall model to form the full flight envelope model. Second, the pitching moment model, which is used in conjunction with the Kirchhoff lift model, cannot accurately describe the aircraft stall pitching moment characteristics. To ensure the compatibility of the two models, this paper proposes a method to determine some unknown parameters in the stall model. The mechanism for the pitching moment generation of the aircraft stall is analyzed, and two high-order correction terms are added to the pitching moment model to better describe the longitudinal stall aerodynamic characteristics. Based on the identification of aerodynamic parameters, a longitudinal stall aerodynamic modeling method used for the aircraft stall process is developed. The identification and simulation validation results based on the quasi-steady stall flight data of a civil aircraft show that the improved stall model can accurately describe the quasi-steady stall pitching moment. The established stall aerodynamic model can accurately characterize the longitudinal quasi-steady stall aerodynamic characteristics of aircraft under different stall degrees.

Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

The firing accuracy of the projectile has a positive relation with aerodynamic parameters. Due to the complex dynamic characteristics of projectiles, there is an overfitting risk when a single extreme learning machine (ELM) is used to identify the aerodynamic parameters of the projectile, and the identification results oscillate transonic region. To obtain the aerodynamic parameters of the projectile accurately, an aerodynamic parameter identification model based on ensemble learning theory and ELM optimized by improved particle swarm optimization is proposed. The improved particle swarm optimization algorithm (IPSO) with an adaptive update strategy is used to optimize the weight and threshold of ELM. Combined with the ensemble learning theory, the improved ELM neural network is regarded as a weak learner to generate a strong learner. The structural parameters of the strong learner were continuously optimized through training, and an aerodynamic parameter identification model of projectile based on ensemble learning theory is obtained. The simulation results show that the introduction of the IPSO and ensemble learning theory enables the model to exhibit excellent generalization ability. The proposed identification model can accurately describe the variation of aerodynamic parameters with the Mach number.

Research on Adaptive Prescribed Performance Control Method Based on Online Aerodynamics Identification

Wide-speed-range vehicles are characterized by high flight altitude and high speed, with significant changes in the flight environment. Due to the strong uncertainty of its aerodynamic characteristics, higher requirements are imposed on attitude control. In this paper, an adaptive prescribed performance control method based on online aerodynamic identification is proposed, which consists of two parts: an online aerodynamic parameter identification method and an adaptive attitude control method based on the pre-defined parameters of the control system. The aerodynamic parameter identification is divided into offline design and online design. In the offline design, neural networks are used to fit nonlinear aerodynamic characteristics. In the online design, a nonlinear recursive identification method is used to correct the errors of the offline fitted model. The adaptive attitude control is based on the conventional control method and updates the control gain in real time according to the desired system parameters to enhance the robustness of the controller. Finally, the effectiveness of the offline neural network and online discrimination correction is verified by mathematical simulations, and the effectiveness and robustness of the adaptive control proposed in this paper are verified by comparative simulation.

Longitudinal Aerodynamic Parameter Identification for Blended-Wing-Body Aircraft Based on a Wind Tunnel Virtual Flight Test

The wind tunnel virtual flight test realizes the dynamic semi-free flight of the model in the wind tunnel through the deflections of the control surface and uses the test data to identify the aerodynamic derivatives. The difference in dynamics between the wind tunnel virtual flight and the free flight leads to discrepancies between the identification and theoretical results. To solve the problems, a step-by-step identification and correction method for aerodynamic derivatives is established based on the difference between the equations of motion of wind tunnel virtual flight and free flight to identify and correct the lift, drag derivatives, pitch moment derivatives, and velocity derivatives, respectively. To establish an aerodynamic parameter identification model, the flight dynamics equation is expressed as a decoupled form of the free flight force and the influence of the test support frame force on the model’s motions through linearization. To ensure the identification accuracy of each aerodynamic derivative, an excitation signal design method based on amplitude–frequency characteristic analysis is proposed. The longitudinal aerodynamic parameter identification results of a blended-wing-body aircraft show that identification results with higher accuracy can be obtained by adopting the proposed identification and correction method.