A method for correcting parameter errors of a helicopter flight dynamics model

Abstract

The application discloses a helicopter flight dynamics model parameter error correction method, comprising the following steps: S1, parameterizing the discrete form of helicopter aerodynamic data to obtain multiple aerodynamic parameters and form a flight dynamics parameterization model; S2, performing trimming error analysis on the parameterization model, determining each main input parameter through sensitivity analysis, and using a trimming error value matrix and a trimming error fitting coefficient matrix about the forward flight speed to represent the model trimming error; and S3, taking the trimming error value matrix and the trimming error fitting coefficient matrix as the input of a neural network to identify each error. The application parameterizes the discrete aerodynamic data to obtain the flight dynamics parameterization model, makes the parameters more explicit, adopts the input parameter error cross combination mode to calculate and generate training samples, and identifies the error of each main input parameter by using the neural network, so that the calculation cost is effectively reduced, and the correction efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of helicopter flight mechanics, and in particular to a method for correcting parameter errors of a helicopter flight dynamics model. Background Art

[0002] Helicopter flight dynamics models serve as the foundation for flight characteristics analysis and flight quality assessment during the helicopter design phase. They are crucial for overall helicopter layout design and flight control system design. Improving the accuracy of flight dynamics modeling is crucial for accurately assessing helicopter flight qualities and designing advanced flight control systems.

[0003] Currently, the mainstream approach to helicopter dynamics modeling is mechanistic modeling, which offers advantages such as clear model parameters, structure, and physical meaning. However, in practice, mechanistic modeling also faces numerous challenges. First, the rotor aerodynamic model, as the core component of the helicopter flight dynamics model, has complex aerodynamic characteristics and states, making it difficult to accurately calculate. Second, accurately calculating the aerodynamic interference between helicopter components is also challenging. Furthermore, the kinematic, inertial, and operational couplings inherent in helicopters pose challenges to accurately modeling helicopter flight dynamics. Due to the combined influence of these factors, flight dynamics models established using mechanistic modeling are not highly accurate. Further improving the accuracy of flight dynamics modeling requires verification and correction of parameter errors within the model.

[0004] Parameter errors in helicopter flight dynamics models can be categorized into three main areas: First, the assumptions and simplifications used during the modeling process introduce errors into the model itself, known as structural errors. These errors include, for example, the quasi-steady state assumption and the rigid blade assumption. Second, errors arise from discrepancies between model input parameters and their true values, known as parameter errors. These errors can include inaccurate or even unknown values ​​for some design parameters during modeling, or inaccurate aerodynamic data derived from wind tunnel tests or CFD calculations. Third, measurement errors can arise between the flight test data used to verify the model's accuracy and the helicopter's actual flight characteristics.

[0005] The current approach to correcting parameter errors in helicopter flight dynamics models primarily relies on flight test data. Using equilibrium equations derived from modeling and prior experience with model corrections, the error sources are identified. The model is then manually corrected to improve accuracy. However, this approach is inefficient and struggles to achieve optimal correction results when model errors are large or complex. Therefore, an efficient and accurate method for correcting helicopter flight dynamics models is needed. Summary of the Invention

[0006] In response to the above problems, the purpose of the present invention is to propose a method for correcting parameter errors of a helicopter flight dynamics model, parameterize discrete aerodynamic data, obtain a flight dynamics parameterized model, and make the model parameters and their meanings clearer; at the same time, the main parameters affecting the model balancing error are determined by sensitivity analysis, which effectively reduces the difficulty of parameter error correction; the model balancing error is characterized by a balancing error value matrix and a balancing error fitting coefficient matrix, and the important information in the flight dynamics model balancing error is accurately extracted; in addition, a neural network algorithm is used to identify the model input parameter error, which can efficiently and accurately identify the input parameter error, thereby improving the efficiency and accuracy of model correction.

[0007] This is achieved through the following technical solutions:

[0008] A method for correcting helicopter flight dynamics model parameter errors is disclosed. The method corrects model errors caused by multiple input parameters with errors. The multiple input parameters include existing model parameters and multiple aerodynamic parameters obtained by parameterizing aerodynamic data. The method comprises the following steps:

[0009] S1. Parameterizing aerodynamic data of a plurality of discrete data points to obtain a plurality of aerodynamic parameters and form a helicopter flight dynamics parameterized model; wherein the aerodynamic data includes aerodynamic force data and aerodynamic interference data;

[0010] S2. Performing a trim error analysis on the helicopter flight dynamics parameterized model in step S1, first determining each major input parameter that affects the model trim error through a sensitivity analysis method, and then characterizing the model trim error using a trim error value matrix and a trim error with respect to forward flight speed fitting coefficient matrix;

[0011] S3. Using the trim error value matrix and the trim error-forward speed fitting coefficient matrix in step S2 as inputs to a neural network, the neural network is trained to identify the error of each major input parameter, and then correcting each major input parameter that has an error based on each error.

[0012] By parameterizing a large number of discrete data points, the helicopter flight dynamics model can better capture actual physical phenomena and make subsequent analysis more intuitive and efficient. At the same time, the trim error of the model is characterized by using a trim error value matrix and a trim error fitting coefficient matrix with respect to forward flight speed. This can effectively obtain the error of the helicopter flight dynamics model at any forward flight speed, making the subsequent correction effect more comprehensive. In addition, neural network training is used to accurately identify the error of each major input parameter and make accurate corrections.

[0013] Preferably, in step S1, the aerodynamic force data includes the airfoil aerodynamic coefficients and each aerodynamic force coefficient and each moment coefficient of each component, and the aerodynamic interference data includes the aerodynamic interference factor of the rotor on each component, the aerodynamic interference factor of the fuselage on the tail surface, and the dynamic pressure loss coefficient of the tail surface and tail rotor. By including the airfoil aerodynamic force coefficients and each aerodynamic force coefficient and each moment coefficient of each component, the aerodynamic characteristics of each helicopter component can be more accurately described. The introduction of the aerodynamic interference data takes into account the mutual interference between different components.

[0014] Preferably, before performing parameterization processing, according to the variation law of the aerodynamic data on the angle of attack or sideslip angle, within a small angle range, the data of each relevant data point on the angle of attack or sideslip angle in the aerodynamic data is fitted using a polynomial function of the highest second order to obtain each corresponding aerodynamic parameter; for each relevant data point on the angle of attack or sideslip angle in the aerodynamic data, the data of each relevant data point on the angle of attack or sideslip angle in the aerodynamic data is fitted according to the formula within a small angle of attack or sideslip angle range. Processing is performed, wherein n is a non-negative integer, x is the angle of attack or sideslip angle corresponding to the data of each relevant data point above, is the error between the function fitting value and the corresponding arbitrary aerodynamic data before fitting, The corresponding aerodynamic parameters are obtained by parameterizing the aerodynamic data. The angle of attack and sideslip angle are important factors affecting the helicopter flight dynamics model during actual operation. Fitting within a small angle range can effectively ensure the efficiency and effectiveness of the model calculation.

[0015] Preferably, when performing parameterization processing, the data of multiple data points in the aerodynamic interference data corresponding to each component of the rotor are first parameterized. The parameterization processing is the starting point, turning point and ending point of the interference factor curve corresponding to each component, as well as the wake tilt angle and interference factor value corresponding to the ending point. Each component includes the fuselage, tail surface and tail rotor. Then, the above points are used to interpolate the wake tilt angle to obtain the aerodynamic interference factor of the rotor on each component. The method of capturing the key characteristic points of the interference factor curve is adopted. The wake tilt angle and interference factor value at the three key positions of the curve, the starting point, turning point and ending point, are parameterized to ensure that the model can capture the main change trends and characteristics of the aerodynamic interference; at the same time, it provides convenience for the setting of aerodynamic parameter errors. When the errors of the aerodynamic parameters corresponding to the above key characteristic points are set, the changes of the aerodynamic interference factor can be reasonably and accurately estimated and described.

[0016] Preferably, in step S2, the sensitivity analysis method is the Monte Carlo integral improved Sobol method. When the Monte Carlo integral improved Sobol method is adopted, multiple input parameters are sampled by quasi-Monte Carlo to obtain corresponding input sample matrices A and B. , , where N is the number of samples and P is the number of sampled input parameters; replace the i-th column of the sample matrix B with the i-th column of the sample matrix A to obtain the matrix ; Substitute the matrix ABi into the parameterized model to obtain f(A), f(B) and f(ABi); The sensitivity calculation formula for the i-th input parameter is , V is the total variance of the outputs f(A) and f(B) corresponding to sample matrices A and B. Compared with the ordinary Monte Carlo sampling method, the Monte Carlo integration improved Sobal method can further improve the accuracy of the results and enhance the accuracy of error analysis.

[0017] Preferably, when performing the trim error analysis in step S2, the trim result when each main input parameter is at the initial value is used as a benchmark, and then the benchmark is subtracted from the trim result when the input parameter has an error to obtain a model trim error; the model trim error includes multiple trim quantities with errors, and the multiple trim quantities include rotor collective pitch, lateral cyclic pitch, longitudinal cyclic pitch, tail rotor collective pitch, roll attitude angle, pitch attitude angle and rotor required power, and the error of each trim quantity together constitutes a trim error value matrix.

[0018] Preferably, when setting the error of each main input parameter, each error is set by using an equal percentage value relative to the reference value of each main input parameter change, and the expression for setting any error is: , is the value when there is an error in any of the main parameters, is the initial value of any main parameter, is the reference value of change, The error is set as a percentage relative to the reference value of each major input parameter, which facilitates active adjustment of the percentage. The size of the error is simplified, making error analysis easier and unifying the errors of the main input parameters of different magnitudes and units under a common standard for description. This allows the errors of parameters of large or small values ​​to be understood and handled in the same way.

[0019] Preferably, during the fitting process, a cubic polynomial is used to fit the trim error values ​​in the trim error matrix with respect to forward flight speed, thereby obtaining a trim error with respect to flight speed fitting coefficient matrix. Using a cubic polynomial for fitting can effectively obtain the error of the helicopter flight dynamics model at any speed. Furthermore, the cubic polynomial can effectively express the relationship between trim error and forward flight speed.

[0020] Preferably, the neural network used in step S3 is a deep belief network or a BP neural network. The deep belief network has good generalization ability, that is, it can improve the accuracy of recognition errors, and the BP neural network is easy to adjust the network structure according to the actual error situation, thereby improving the correction effect.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The technical solution of the present invention performs parameterized processing on aerodynamic data in discrete form to obtain a parametric flight dynamics model, making the model parameters and their meanings clearer; at the same time, the main parameters affecting the model balancing error are determined through a sensitivity analysis method, effectively reducing the difficulty of parameter error correction; the model balancing error is characterized by a balancing error value matrix and a balancing error fitting coefficient matrix, and important information in the flight dynamics model balancing error is accurately extracted; in addition, a neural network algorithm is used to identify the model input parameter error, which can efficiently and accurately identify the input parameter error and improve the efficiency and accuracy of model correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The flowchart of a helicopter flight dynamics model parameter error correction method is as follows;

[0024] Figure 2 A comparison chart of the trim calculation results before and after parameterization of a helicopter flight dynamics model and flight test data;

[0025] Figure 3 A comparison chart of the trim calculation results and flight test data of a helicopter flight dynamics model before and after parameter error correction;

[0026] Figure 4 A schematic diagram of a trim error matrix;

[0027] Figure 5 A schematic diagram of the trim error fitting coefficient matrix with respect to forward flight speed. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0029] like Figure 1 The figure shows a flow chart of a helicopter flight dynamics model parameter error correction method. This method corrects model errors caused by multiple input parameters with errors. The multiple input parameters include existing model parameters and multiple aerodynamic parameters parameterized by aerodynamic data. The existing parameters include data such as gross mass and fuselage length. The method specifically includes the following steps:

[0030] S1. Parameterize aerodynamic data from multiple discrete data points to obtain multiple aerodynamic parameters, thereby forming a parametric model of helicopter flight dynamics. The aerodynamic data includes aerodynamic force data and aerodynamic interference data. These multiple aerodynamic parameters are important parameters of the helicopter flight dynamics model during operation. Parameterizing multiple discrete data points reduces computational complexity, facilitates analysis and processing, and ensures computational accuracy.

[0031] In this embodiment, in step S1, the aerodynamic data includes the airfoil aerodynamic coefficients and each aerodynamic coefficient and each torque coefficient of each component. The aerodynamic interference data includes the aerodynamic interference factor of the rotor on each component, the aerodynamic interference factor of the fuselage on the tail surface, and the dynamic pressure loss coefficient of the tail surface and tail rotor. The airfoil aerodynamic coefficients and each aerodynamic coefficient and each torque coefficient of each component are important coefficients that affect the simulation calculation of the helicopter flight dynamics model. Therefore, parameterizing these coefficients and obtaining each corresponding aerodynamic parameter can more accurately describe the aerodynamic characteristics of each helicopter component. The introduction of aerodynamic interference data takes into account the mutual interference between different components and is also an important coefficient that affects the simulation calculation of the helicopter flight dynamics model.

[0032] In this embodiment, before performing parameterization processing, the data of each relevant data point on the angle of attack or sideslip angle in the aerodynamic data can be fitted using a polynomial function of the highest second order within a small angle range according to the variation law of the aerodynamic data on the angle of attack or sideslip angle to obtain each corresponding aerodynamic parameter; for the data of each relevant data point on the angle of attack or sideslip angle in the aerodynamic data, the corresponding aerodynamic parameter can be obtained according to the formula within a small angle of attack or sideslip angle range. Processing is performed, where f(x) is the fitting function expression, n is a non-negative integer, and x is the angle of attack or sideslip angle corresponding to the data of each relevant data point above. is the error between the function fitting value and the corresponding arbitrary aerodynamic data before fitting, The corresponding aerodynamic parameters are obtained by parameterizing the aerodynamic data. The angle of attack and sideslip angle are important factors affecting the helicopter flight dynamics model during actual operation. Fitting within a small angle range can effectively ensure the efficiency and effectiveness of the model calculation.

[0033] It should be noted that the small angle range generally refers to the angle of attack or sideslip angle not exceeding ±10 degrees to ±15 degrees. Within this range, the aerodynamic characteristics usually exhibit relatively linear behavior, so a polynomial function of the highest second order can be used to well approximate the actual data.

[0034] In this embodiment, when performing parameterization processing, the data of multiple data points in the aerodynamic interference data of the rotor to each component needs to be parameterized first. The parameterization processing is the start point, turning point and end point of the interference factor curve corresponding to each component respectively, and the wake tilt angle and interference factor value corresponding to the end point. Each component includes the fuselage, tail surface and tail rotor. Then, the data between the above three points is linearly interpolated by the wake tilt angle to form the interference factor curve, so that the aerodynamic interference factor of the rotor to each component can be obtained. At the same time, the parameterization method of the aerodynamic interference factor of the fuselage to the tail surface is the same as that of the aerodynamic interference factor of the rotor to each component, and the difference lies in the name of the object; the parameterization method of the dynamic pressure loss coefficient of the tail surface and the tail rotor is the same as that of the aerodynamic interference factor of the rotor to each component, and the difference lies in the name of the object. By capturing the key feature points of the interference factor curve, the wake tilt angle and the interference factor value at the three key positions of the curve start point, turning point and end point are parameterized to describe, which can ensure that the model can capture the main change trend and characteristics of the aerodynamic interference; at the same time, it provides convenience for the error setting of the aerodynamic parameters, and when the error of the aerodynamic parameters corresponding to the above key feature points is set, the change of the aerodynamic interference factor can be reasonably and accurately estimated and described.

[0035] It should be noted that the key feature points obtained after parameterization of the aerodynamic interference data are not as many as the original discrete data points, nor are they continuous. The main purpose is to reduce the number of parameters on the basis of retaining the main change trend and characteristics of the aerodynamic interference, facilitate the error setting of related parameters, and reasonably and accurately describe the change of the aerodynamic interference factor when there is an error in the parameters.

[0036] S2, trim error analysis is performed on the helicopter flight dynamics parameterization model in step S1. First, the sensitivity analysis method is used to determine each main input parameter affecting the model trim error, and then the trim error value matrix and the trim error fitting coefficient matrix about the forward flight speed are used to represent the model trim error.

[0037] In this embodiment, in step S2, the sensitivity analysis method is the Monte Carlo integral improved Sobol method. When the Monte Carlo integral improved Sobol method is used, the input sample matrix A and B corresponding to multiple input parameters are obtained by quasi-Monte Carlo sampling, , wherein N is the sample number, and P is the number of sampled input parameters; the i-th column of the sample matrix B is replaced with the i-th column of the sample matrix A to obtain the matrix ; the matrix AB i is substituted into the parameterization model to obtain f(A), f(B) and f(AB i ); and the sensitivity calculation formula of the i-th input parameter is , V is the total variance of the outputs f(A) and f(B) corresponding to sample matrices A and B. Compared with the ordinary Monte Carlo sampling method, the Monte Carlo integration improved Sobol method can further improve the accuracy of the results and enhance the accuracy of error analysis.

[0038] In this embodiment, when performing the trim error analysis in step S2, the trim result when each main input parameter is at the initial value is used as a benchmark, and then the benchmark is subtracted from the trim result when the input parameter has an error to obtain a model trim error; the model trim error includes multiple trim quantities with errors, and the multiple trim quantities include rotor collective pitch, lateral cyclic pitch, longitudinal cyclic pitch, tail rotor collective pitch, roll attitude angle, pitch attitude angle, and required rotor power. The error of each trim quantity together constitutes a trim error value matrix.

[0039] like Figure 2 The figure below compares the trim calculation results of the helicopter flight dynamics model before and after parameterization with flight test data. The comparison verifies the accuracy of the parameterization process by measuring rotor collective pitch, lateral cyclic pitch, longitudinal cyclic pitch, tail rotor collective pitch, roll angle, pitch angle, and required rotor power. The AEFA test flight data is flight test data. Before and after parameterization, the trim calculations of the original model and the parameterized model must be consistent to ensure the accuracy of subsequent calculations.

[0040] S3. Using the trim error value matrix and the trim error-forward speed fitting coefficient matrix in step S2 as inputs to a neural network, the neural network is trained to identify the error of each major input parameter, and then correcting each major input parameter that has an error based on each error.

[0041] In this embodiment, when setting the error of each main input parameter, each error is set by using an equal percentage value relative to the reference value of each main input parameter change. The expression for setting any error is: , is the value when there is an error in any of the main parameters, is the initial value of any main parameter, is the reference value of change, The error is set as a percentage relative to the reference value of each major input parameter, which facilitates active adjustment of the percentage. The size of the error makes error analysis easier and the errors of the main input parameters of different magnitudes and units can be described under a common standard, that is, they can be calculated on the same scale. This makes it possible to understand and handle the errors of any main input parameters, whether large or small, in the same way, and avoids calculation errors caused by different numerical dimensions.

[0042] In this embodiment, a cubic polynomial is used to fit the trim error values ​​in the trim error matrix with respect to forward flight speed, resulting in a fitting coefficient matrix of trim error with respect to flight speed. Using a cubic polynomial for fitting effectively captures the error of the helicopter flight dynamics model at any speed. Furthermore, the cubic polynomial effectively expresses the relationship between trim error and forward flight speed.

[0043] like Figure 4 As shown in the figure, it is a schematic diagram of the balancing error value matrix, which contains data from e11 to e713, a total of 7 rows and 13 columns, as shown in the figure. Figure 5 The figure is a schematic diagram of the trim error with respect to the forward flight speed fitting coefficient matrix. Figure 4 The result of fitting the trim error matrix with a cubic polynomial is 7 rows and 8 columns of data from P11 to P78. Since a cubic polynomial is used for fitting, the order of each data item in the trim error fitting coefficient matrix for forward flight speed also ranges from the third-order term to the constant term. Figure 4 and Figure 5 As shown, in the trim error matrix, each row is the trim error, which corresponds to the rotor collective pitch, lateral cyclic pitch, longitudinal cyclic pitch, tail rotor collective pitch, roll attitude angle, pitch attitude angle and rotor required power. The roll attitude angle is also called the roll angle, and the pitch attitude angle is also called the pitch angle. Each column corresponds to a different forward flight speed between 0m / s and 80m / s. After fitting each error data in the trim error matrix with respect to the forward flight data, the fitted coefficient matrix can be obtained. In the coefficient matrix obtained after fitting, each row is respectively Figure 4 The trim error fitting coefficients corresponding to different trim errors in the . After forming the trim error matrix, the trim error value matrix and the forward flight speed are fitted using a cubic polynomial to obtain the coefficient matrix. This can effectively obtain the error of the helicopter flight dynamics model at any speed. At the same time, the cubic polynomial can effectively express the relationship between the trim error and the forward flight speed.

[0044] In this embodiment, the neural network used in step S3 has multiple options, such as a deep belief network or a BP neural network. The deep belief network has good generalization ability, that is, it can improve the accuracy of recognition errors, and the BP neural network is convenient for adjusting the network structure according to the actual error situation, thereby improving the correction effect. Taking the use of a deep belief network as an example, the deep belief network can use the restricted Boltzmann machine RBM as the network core, and use the gradient descent method to update the data of different layers in the network. The RBM network in the restricted Boltzmann machine RBM is a two-layer structure, the lower layer is the visible layer, and the upper layer is the hidden layer. Let the number of nodes in the visible layer of the RBM network be q, and the input be , the node offset is ; The number of hidden layer nodes is m, and the output is , the node offset is , the visual layer and the hidden layer are connected by a weight matrix The activation function of neurons in each layer of the RBM network uses the sigmoid function. First, define the energy function of the input and output of the RBM network as:

[0045]

[0046] Then the joint probability distribution between the visible layer and the hidden layer is It can be expressed as: , in the above formula, is the partition function.

[0047] RBM networks also have pre-training, which uses the output of the previous layer as the input of the next layer and obtains a stable network structure through layer-by-layer training. The optimal parameters of the RBM network can be obtained by maximizing the log-likelihood function:

[0048]

[0049] Then, by using the contrastive divergence fast learning method for each training sample, the training speed is fast and the training efficiency is high. The relevant parameters of the RBM network are updated as follows during training:

[0050]

[0051] In the above formula, is the learning rate.

[0052] After training, the deep belief network is fine-tuned. Using a multi-layer feedforward network trained with error backpropagation, the network is continuously adjusted through error signals to achieve overall optimization. The input is processed by each layer of RBM and backpropagation layers to calculate the output. The error between the network output and the target output is backpropagated layer by layer, and gradient descent is used to continuously fine-tune and update the parameters of each layer of the network. The weights are updated as follows: , in the above formula, is the gradient of the k-th layer neuron, is the output of the k-1th layer of neurons, and n is the number of training times.

[0053] Specific application example 1:

[0054] During the error identification of the input parameters of a helicopter flight dynamics model, the main input parameters affecting the model trim error were determined through sensitivity analysis, including: v1-longitudinal position of the center of gravity, v2-vertical tail installation angle, v3-flapping hinge offset, v4-advance control angle, v5-tail rotor induced speed correction coefficient, v6-rotor induced speed correction coefficient, v7-fuselage zero angle of attack drag coefficient, v8-horizontal tail lift line slope, v9-rotor airfoil zero lift drag coefficient, v10-rotor lift line slope and v11-rotor vertical interference with the horizontal tail.

[0055] The generation of training samples for the neural network is shown in Table 1 below:

[0056] Table 1: Neural network training sample table

[0057]

[0058] In Table 1, all parameters are less than 0%, corresponding to the case of negative error; all parameters are 0%, corresponding to the case of no error; all parameters are greater than 0%, corresponding to the case of positive error; when setting the percentage of change, it is necessary to comprehensively consider three different situations and conduct separate training for multiple groups, so that the final calculated results can correspond to more actual situations and improve accuracy.

[0059] To match the data format required for neural network training, the columns of the trimmed error value matrix and the trimmed error fitting coefficient matrix were transposed and then concatenated to form a row matrix, which served as the neural network input. The row matrix consisting of the percentage change in each model input parameter served as the network output. The input data dimensions of the two neural networks were 7 × 13 = 91 and 7 × 8 = 56, respectively, and the network output data dimensions were both 11.

[0060] Secondly, to avoid affecting the prediction performance of the neural network, the sample data after structural transformation is dimensionally normalized so that the data of each dimension are in the same range. Dimensional normalization generally uses the maximum and minimum values ​​of each dimension of the sample data to normalize the original data to between [-1, 1] or [0, 1]. The relevant expressions are as follows:

[0061]

[0062] The generated samples from groups 1 to 7 were used as training sets, and the trim error value and the trim error-to-velocity fitting coefficient were used as inputs to the neural network. The BP neural network and the deep belief network (DBN) were trained separately. After network training, the 8th group of samples was used as the validation set. The maximum absolute error between the predicted value and the true value of the percentage change of each parameter is shown in Table 2 below:

[0063] Table 2: Maximum absolute error between the predicted values ​​and the true values ​​of the percentage change of each parameter in the two networks

[0064]

[0065] From the comparison of the results in Table 2 above, it can be seen that the maximum absolute error between the predicted value and the true value of the percentage change of each input parameter does not exceed 1% overall, indicating that the method of the present invention can accurately identify the error of the input parameter through the model balancing error.

[0066] Specific application example 2:

[0067] Taking the BO105 helicopter flight dynamics model with poor accuracy as an example, the above method is used to correct it. The specific correction process is as follows:

[0068] First, we assume that a set of input parameter values ​​are accurate, and use the model's trim results for this set of input parameters as the baseline values. Then, using the same training sample generation method as in Example 1, we calculate seven sets of 895 training samples, each with ±10% and ±20% percentage changes in the input parameters, for neural network training. Finally, the error between the assumed baseline values ​​and the flight test data is input into the neural network to identify the input parameter errors, thereby completing the correction of the flight dynamics model. The model correction parameters, along with the initial and corrected values ​​of each parameter, are shown in Table 3 below:

[0069] Table 3: Model correction parameters and initial and corrected values ​​of each parameter

[0070]

[0071] like Figure 3 The figure shows a comparison of the trim calculation results of the helicopter flight dynamics model before and after parameter error correction with flight test data. Specifically, this figure compares flight test data before and after correction of the BO105 helicopter flight dynamics model based on the data in Table 3, verifying the accuracy of the helicopter flight dynamics model after error correction. As can be seen from the figure, the model trim results after parameter error correction more closely match the flight test data, significantly improving accuracy and demonstrating the feasibility of the helicopter flight dynamics model parameter error correction method of the present invention.

[0072] In summary, the present invention parameterizes a large number of discrete data points, so that the helicopter flight dynamics model can better capture actual physical phenomena and make subsequent analysis more intuitive and efficient; at the same time, the model trim error is characterized by using a trim error value matrix and a trim error fitting coefficient matrix with respect to forward flight speed, which can effectively obtain the error of the helicopter flight dynamics model at any forward flight speed, making the subsequent correction effect more comprehensive; in addition, neural network training is also used to accurately identify the error of each main input parameter, and then accurately correct it, which is significantly progressive.

[0073] The above embodiments are only for illustrating the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A helicopter flight dynamics model parameter error correction method, characterized in that: The method corrects model errors caused by multiple input parameters with errors, wherein the multiple input parameters include existing parameters of the model and multiple aerodynamic parameters obtained by parameterizing aerodynamic data. The method comprises the following steps: S1. Parameterizing aerodynamic data of a plurality of discrete data points to obtain a plurality of aerodynamic parameters and form a helicopter flight dynamics parameterized model; wherein the aerodynamic data includes aerodynamic force data and aerodynamic interference data; S2. Performing a trim error analysis on the helicopter flight dynamics parameterized model in step S1, first determining each major input parameter that affects the model trim error through a sensitivity analysis method, and then characterizing the model trim error using a trim error value matrix and a trim error with respect to forward flight speed fitting coefficient matrix; S3. Using the trim error value matrix and the trim error-forward speed fitting coefficient matrix in step S2 as inputs to a neural network, the neural network is trained to identify the error of each major input parameter, and then correcting each major input parameter that has an error based on each error.

2. A helicopter flight dynamics model parameter error correction method according to claim 1, characterized in that: In step S1, the aerodynamic data includes the airfoil aerodynamic coefficient and each aerodynamic coefficient and each moment coefficient of each component, and the aerodynamic interference data includes the aerodynamic interference factor of the rotor on each component, the aerodynamic interference factor of the fuselage on the tail surface, and the dynamic pressure loss coefficient of the tail surface and the tail rotor.

3. The helicopter flight dynamics model parameter error correction method according to claim 1, characterized in that: Before parameterization, according to the variation law of the aerodynamic data with respect to the angle of attack or sideslip angle, a polynomial function of the highest second order is used to fit the data of each relevant data point of the aerodynamic data with respect to the angle of attack or sideslip angle within a small angle range to obtain each corresponding aerodynamic parameter. For each relevant data point of the angle of attack or sideslip angle in the aerodynamic data, in the range of small angle of attack or sideslip angle, according to the formula Processing is performed, wherein n is a non-negative integer, x is the angle of attack or sideslip angle corresponding to the data of each relevant data point, is the error between the function fitting value and the corresponding arbitrary aerodynamic data before fitting, These are the corresponding aerodynamic parameters obtained by parameterizing the aerodynamic data.

4. The helicopter flight dynamics model parameter error correction method according to claim 2, characterized in that: When performing parameterization processing, the data of multiple data points in the aerodynamic interference data corresponding to each component of the rotor are first parameterized. The parameterization processing is the starting point, turning point and end point of the interference factor curve corresponding to each component, as well as the wake tilt angle and interference factor value corresponding to the end point. Each component includes the fuselage, tail surface and tail rotor. Then, the aerodynamic interference factor of the rotor on each component is obtained by interpolating the wake tilt angle using the above points. When performing parameterization processing, the parameterization method of the aerodynamic interference factor of the fuselage on the tail surface is the same as the parameterization method of the aerodynamic data; the parameterization method of the dynamic pressure loss coefficient of the tail surface and tail rotor is the same as the parameterization method of the aerodynamic interference factor of the rotor on each component.

5. The helicopter flight dynamics model parameter error correction method according to claim 1, characterized in that: In step S2, the sensitivity analysis method is the Monte Carlo integral improved Sobol method. When the Monte Carlo integral improved Sobol method is used, multiple input parameters are sampled by quasi-Monte Carlo to obtain corresponding input sample matrices A and B. , , where N is the number of samples and P is the number of sampled input parameters; replace the i-th column of the sample matrix B with the i-th column of the sample matrix A to obtain the matrix ; The matrix AB i Substitute into the parameterized model and obtain f(A), f(B) and f(AB i ); The sensitivity calculation formula of the i-th input parameter is , V is the total variance of the outputs f(A) and f(B) corresponding to the sample matrix A and the sample matrix B.

6. The helicopter flight dynamics model parameter error correction method according to claim 1, characterized in that: When performing trim error analysis in step S2, the trim result when each main input parameter is at an initial value is used as a benchmark, and then the benchmark is subtracted from the trim result when the input parameters have errors to obtain a model trim error. The model trim error includes multiple trim quantities with errors, and the multiple trim quantities include rotor collective pitch, lateral cyclic pitch, longitudinal cyclic pitch, tail rotor collective pitch, roll attitude angle, pitch attitude angle, and required rotor power. The errors of each trim quantity together constitute a trim error value matrix.

7. The helicopter flight dynamics model parameter error correction method according to claim 1, characterized in that: When setting the error of each main input parameter, use the same percentage value relative to the reference value of each main input parameter to set each error. The expression for setting any error is: , is the value when there is an error in any of the main parameters, is the initial value of any main parameter, is the reference value of change, As a percentage.

8. The helicopter flight dynamics model parameter error correction method according to claim 1, characterized in that: When performing the fitting process, a cubic polynomial is used to fit the trim error values ​​in the trim error value matrix with respect to the forward flight speed, and a fitting coefficient matrix of the trim error with respect to the flight speed is obtained.

9. The helicopter flight dynamics model parameter error correction method according to claim 1, characterized in that: The neural network used in step S3 is a deep belief network or a BP neural network.

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