Method for estimating combination characteristics of runway and tire

Abstract

The invention discloses a runway and tire combination characteristic estimation method, and particularly relates to the field of aircraft anti-skid brake control. Comprising the steps of obtaining state information of a current flight anti-skid braking system; establishing a runway / tire combination characteristic model according to the state information of the anti-skid braking system of the current flight; acquiring brake state data of a preorder flight, wherein the brake state data comprises a median of a peak value combination coefficient of a runway / tire and an optimal slip rate; a state observation method is adopted, the braking state data of the previous flight serve as an initial state variable, the runway / tire combination characteristic model serves as a state equation and an observation equation, and the braking state data of the current flight in the braking process are estimated; and outputting the braking state data of the current flight in the braking process as runway / tire combination characteristics. And the real runway / tire combination characteristics can be quickly and accurately obtained on the premise of preventing the airplane wheels from slipping.

Description

Technical Field

[0001] This application relates to the field of aircraft anti-skid brake control, and particularly to a method for estimating the combined characteristics of a runway and tires. Background Art

[0002] The aircraft anti-skid brake system is one of the important airborne subsystems, ensuring the safety of the aircraft during takeoff and landing roll. As the decision-making core of the aircraft anti-skid brake system, the anti-skid brake control law is a key factor affecting the anti-skid brake performance of the aircraft. The control objective of the aircraft anti-skid brake system is to adjust the brake torque to match the runway combined characteristics and maximize the maximum combined torque provided by the runway. Therefore, a superior anti-skid brake control law should be able to estimate the current runway peak combined coefficient and the optimal slip rate in real time, and adjust the brake pressure to control the real-time slip rate near the optimal slip rate, that is, keep the anti-skid brake system operating at the best working position. Accurately estimating the runway / tire combined characteristics becomes a prerequisite for the design of an advanced anti-skid brake control law.

[0003] However, the combined characteristics of the runway / tire of an aircraft cannot be directly measured and are complex and variable. The runway surface conditions (dry, wet, waterlogged, snow, melting snow, ice, etc.), wheel loads, tire inflation pressure, etc. will all have a significant impact on the peak combined coefficient of the runway / tire and the corresponding optimal slip rate, posing challenges to the estimation of the runway / tire combined characteristics. In current research, designing a state observer based on the tire dynamics model has become a common method, such as the extended Kalman filter method and the unscented Kalman filter method. However, the state observation method often relies on sufficient excitation conditions and often has disadvantages such as slow convergence or convergence to a local optimal solution when estimating a nonlinear system, resulting in inaccurate estimation of the runway / tire combined characteristics and unable to meet the requirements of the design of an advanced anti-skid brake control law. Summary of the Invention

[0004] The main purpose of this application is to provide a method for estimating the combined characteristics of a runway and tires, aiming to solve the problem that the existing estimation methods cannot accurately estimate the runway / tire combined characteristics.

[0005] To achieve the above objective, this application provides a method for estimating the combined characteristics of a runway and tires, including: obtaining the state information of the anti-skid brake system of the current flight; establishing a runway / tire combined characteristics model according to the state information of the anti-skid brake system of the current flight; obtaining the brake state data of the previous flight, where the brake state data includes the median of the peak combined coefficient of the runway / tire and the optimal slip rate; using the state observation method, taking the brake state data of the previous flight as the initial state variable, and taking the runway / tire combined characteristics model as the state equation and the observation equation to estimate the brake state data of the current flight during the braking process; taking the brake state data of the current flight during the braking process as the output of the runway / tire combined characteristics.

[0006] Optionally, obtain the brake state data of the previous flight, including: when it is determined that the difference between the brake time of the previous flight and the landing time of the current flight is less than or equal to a preset value, obtain the real brake state data of the previous flight, and determine the runway type according to the brake state data of the previous flight; determine the median of the peak combination coefficient and the median of the optimal slip rate corresponding to the current runway type; use the median of the peak combination coefficient and the median of the optimal slip rate as the brake state data of the previous flight.

[0007] Optionally, the brake state data further includes the acceptable maximum brake pressure and the achievable maximum aircraft deceleration rate.

[0008] Optionally, determining the runway type according to the brake state data of the previous flight includes: inputting the brake state data of the previous flight into a runway type identification network to obtain the runway type.

[0009] Optionally, the method for establishing the runway type identification network includes: obtaining different runway types and corresponding brake state data; using the brake state data in the training data set as the input and the corresponding runway type as the output to train a support vector machine to obtain the runway type identification network.

[0010] Optionally, the state information of the anti-skid brake system includes the aircraft speed and the brake pressure; according to the state information of the anti-skid brake system of the current flight, establish a runway / tire combination characteristic model, including: determining the braking torque according to the brake pressure; establishing a wheel dynamics model according to the braking torque, the wheel angular acceleration, and the runway / tire combination coefficient; establishing a slip model according to the wheel angular acceleration, the aircraft speed, and the slip rate; establishing a friction model according to the slip rate and the runway / tire combination coefficient; establishing a runway / tire combination characteristic model according to the wheel dynamics model, the slip model, and the friction model.

[0011] Optionally, the runway / tire combination characteristic model is:

[0012]

[0013] where k is the moment, x = [B C D], B is the stiffness factor, C is the shape factor, D is the peak factor, is the wheel angular acceleration, s is the slip rate, μ is the runway / tire combination coefficient, w k and v k are the system noise and the measurement noise respectively.

[0014] Optionally, the wheel dynamics model is:

[0015]

[0016] where F Nis the wheel load, r m is the wheel rolling radius, k b is the brake pressure - brake torque proportionality coefficient, P b is the brake pressure, J w is the moment of inertia of the wheel.

[0017] Optionally, the slip model is:

[0018]

[0019] In the formula, V p represents the aircraft speed, ω w is the angular velocity of the wheel.

[0020] Optionally, the friction model is:

[0021] μ = D·sin(C·arctan(Bs)).

[0022] Compared with the prior art, the beneficial effects of this application are as follows:

[0023] The estimation method of the runway and tire combination characteristics of the present invention integrates the brake state data of the previous flight and the wheel dynamics state of the current flight, and quickly and accurately estimates the runway / tire combination characteristics and their optimal slip ratio; taking the brake state data of the previous flight as the initial value, using the unscented Kalman filter method to design a state observer to estimate the peak combination coefficient and optimal slip ratio of the runway / tire during the braking process of the current flight, which does not rely on sufficient excitation conditions and avoids falling into local optima, that is, it can quickly and accurately obtain the true runway / tire combination characteristics on the premise of avoiding wheel skidding. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a schematic flow chart of an estimation method for the combination characteristics of a runway and a tire according to this application.

[0025] The realization, functional characteristics and advantages of the purpose of this application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below with reference to the drawings in this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without making creative efforts shall fall within the protection scope of this application.

[0027] The first embodiment of the present invention provides an estimation method for the combination characteristics of a runway and a tire, as Figure 1As shown in the figure, it specifically includes the following steps:

[0028] Step S1, obtain the status information of the current flight anti-skid braking system; among them, the status information of the anti-skid braking system includes the aircraft speed and the braking pressure;

[0029] Step S2, establish a runway / tire combination characteristic model according to the status information of the current flight anti-skid braking system; specifically includes the following steps:

[0030] Step S21, determine the braking torque according to the braking pressure;

[0031] Step S22, establish a wheel dynamics model according to the braking torque, wheel angular acceleration, and runway / tire combination coefficient; the wheel dynamics model is:

[0032]

[0033] In the formula, F N is the wheel load, r m is the wheel rolling radius, M b is the braking torque, J w is the wheel moment of inertia, is the wheel angular acceleration, μ is the runway / tire combination coefficient, k b is the braking pressure-braking torque proportionality coefficient, P b represents the braking pressure.

[0034] Step S23, establish a slip model according to the wheel angular acceleration, aircraft speed, and slip ratio; the slip model is:

[0035]

[0036] In the formula, V p represents the aircraft speed, s is the slip ratio, ω w is the wheel angular velocity, and the wheel angular velocity can be obtained by integrating the wheel angular acceleration.

[0037] Step S24, establish a friction model according to the slip ratio and runway / tire combination coefficient; the friction model is:

[0038] μ = D·sin(C·arctan(Bs));

[0039] In the formula, B is the stiffness factor, controlling the curve slope and determining the initial growth rate of the force / moment; C is the shape factor, determining the curve peak shape; D is the peak factor, determining the maximum value of the combination coefficient.

[0040] During the aircraft braking process, the runway / tire coefficient of adhesion can be regarded as a quantity that varies with time. To obtain the runway / tire coefficient of adhesion, three factors in the friction model are selected as the states of the unscented Kalman filter method, and the wheel angular acceleration is used as the input to establish the runway / tire adhesion characteristic model, which is as follows.

[0041] Step S25, establish a runway / tire adhesion characteristic model according to the wheel dynamics model, slip model, and friction model. The runway / tire adhesion characteristic model is:

[0042]

[0043] In the formula, k is the moment, x = [B C D], w k and v k are the system noise and measurement noise respectively.

[0044] Step S3, obtain the braking state data of the previous flight; among them, the braking state data includes the peak runway / tire coefficient of adhesion, the optimal slip rate, the acceptable maximum braking pressure, and the achievable maximum aircraft deceleration rate.

[0045] In this embodiment, the design of the unscented Kalman filter state observer for the current flight is based on a simple wheel dynamics model and a runway adhesion characteristic model. The formed algorithm is simple and easy to implement, does not require a large amount of computing resources of the anti-skid brake controller, and has engineering application value.

[0046] To ensure the accuracy of the estimation, the braking state data of the previous flight is not directly used as the initial state data. Instead, according to the braking state data of the previous flight, the runway type is judged, and then the median of all braking state data corresponding to the runway type is used as the initial state data. The specific steps are as follows.

[0047] Step S31, when it is determined that the difference between the braking time of the previous flight and the landing time of the current flight is less than or equal to the preset value, determine the runway type according to the braking state data of the previous flight;

[0048] Specifically, in step S311, according to the validity of the runway type result judged by the braking state data of the previous flight, if the landing time of the current flight is within 30 minutes from the braking time of the previous flight, it is determined to be valid; if it exceeds 30 minutes, the judgment result is determined to be invalid. Set the timeliness of the runway classification result of the previous flight to avoid the current flight still using the wrong initial value for calculation after the runway state changes.

[0049] The determination of the runway type is as follows: first, construct a runway type identification network, and then use the runway type identification network to classify the runway type, which is specifically as follows:

[0050] Step S312: Obtain different runway types and corresponding brake state data, and establish a runway type database, as shown in Table 1.

[0051] Table 1 Common runway types and corresponding brake state parameters

[0052]

[0053] Step S313: Divide the runway type database into a training dataset and a test dataset. Use the brake state data in the training dataset as the input and the corresponding runway type as the output to train a support vector machine to obtain a runway type identification network.

[0054] The specific process is as follows: (1) Prepare the training dataset where x i is a vector containing brake state information, and y i is a vector representing the runway type.

[0055] (2) Select the radial basis function as the kernel function, where is the bandwidth parameter of the radial basis kernel function. Map the relationship between the runway type and the corresponding brake state information to a high-dimensional feature space and solve the optimal classification decision boundary in the high-dimensional space.

[0056] (3) Use the cross-validation method to perform hyperparameter tuning on etc., and achieve accurate classification of runway types on the training dataset to obtain a runway type identification network.

[0057] (4) Use the test data to verify the runway type identification network obtained by training. The classification accuracy reaches more than 95%, and finally obtain a reliable and accurate runway type identification network.

[0058] Step S314: Input the brake state data of the previous flight into the runway type identification network to obtain the runway type.

[0059] Step S32: Determine the median of the peak adhesion coefficient and the median of the optimal slip ratio corresponding to the current runway type; use the median of the peak adhesion coefficient and the median of the optimal slip ratio as the brake state data of the previous flight. It can be understood that the median is the middle value of the range interval of the peak adhesion coefficient corresponding to the current runway type.

[0060] Since the runway / tire combination characteristics are affected not only by the runway surface condition, but also by factors such as tire pressure and wheel load, the runway / tire combination characteristics of different flights will also vary under the same runway conditions. To address this issue, the present invention uses the median of the runway combination characteristics obtained from the braking state judgment of the previous flight as the initial value of the state observer for the current flight, enabling the runway combination characteristics estimation method to be applicable to most aircraft types and enhancing practicality.

[0061] It can be understood that the peak combination coefficient is the maximum value of the runway / tire combination coefficient.

[0062] Step S4: Adopt the state observation method, use the braking state data of the previous flight as the initial state variable, use the runway / tire combination characteristics model as the state equation and the observation equation, and estimate the peak runway / tire combination coefficient and the optimal slip ratio during the braking process of the current flight; use the peak combination coefficient and the optimal slip ratio as the runway / tire combination characteristics output. Exemplarily, the state observation method adopted in this embodiment is the unscented Kalman filter method.

[0063] Step S41: Use the median of the peak combination coefficient and the median of the optimal slip ratio obtained in step 3 as the initial state variable, and initialize the covariance matrix P0 using the median of the peak combination coefficient and the median of the optimal slip ratio.

[0064] Step S42: Perform the time update process. Sigma points are created as:

[0065]

[0066] where λ is an important parameter.

[0067] Update the runway / tire combination characteristics model for each Sigma point through the process, and the state prediction and prediction error covariance are:

[0068]

[0069] where W i m and W i c represent the mean weight factor and the variance weight factor respectively.

[0070]

[0071] where σ and η are scale factors, and τ is the high-order term of the historical state information. σ and η satisfy the formula λ = σ 2 (n + η) - n.

[0072] Step S43: Output and optimal state update.

[0073] and After updating, the output and its covariance P y,k are as follows:

[0074]

[0075] Then the covariance P x,y,k is as follows:

[0076]

[0077] Then the posterior state estimate and the covariance matrix are:

[0078]

[0079] Step S44, repeat steps S42 - S43. After the covariance matrix converges, output the peak combination coefficient and the optimal slip ratio of the current flight's braking and skidding runway. Among them, the convergence condition is that the error value between the combination coefficient result of the last iteration and the result of the previous iteration is less than 0.01.

[0080] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A method for estimating the combined characteristics of a track and a tire, characterized in that: include: Get the status information of the anti-skid brake system of the current flight; Establishing a runway / tire combination characteristic model according to the state information of the anti-skid braking system of the current flight; Acquire the braking status data of the preceding flight, wherein the braking status data includes the median of the peak combination coefficient of the runway / tire and the optimal slip rate; The state observation method is adopted, and the braking state data of the previous flight is used as the initial state variable, and the runway / tire combination characteristic model is used as the state equation and the observation equation to estimate the braking state data of the current flight during the braking process; The braking status data of the current flight during the braking process is output as a runway / tire combination characteristic.

2. The method for estimating the combined characteristics of a track and a tire according to claim 1, characterized in that: The obtaining of the brake status data of the preceding flight includes: When it is determined that the difference between the braking time of the preceding flight and the landing time of the current flight is less than or equal to a preset value, obtaining the actual braking status data of the preceding flight, and determining the runway type according to the braking status data of the preceding flight; Determine the median of the peak binding coefficient and the median of the optimal slip ratio corresponding to the current runway type; The median of the peak combination coefficient and the median of the optimal slip rate are used as the braking status data of the preceding flight.

3. The method for estimating the combined characteristics of a track and a tire according to claim 2, characterized in that: The brake status data also includes an acceptable maximum brake pressure and an achievable maximum aircraft deceleration rate.

4. The method for estimating the combined characteristics of a track and a tire according to claim 3, characterized in that: Determining the runway type according to the braking status data of the preceding flight includes: The brake status data of the preceding flight is input into a runway type identification network to obtain the runway type.

5. The method for estimating the combined characteristics of a track and a tire according to claim 4, characterized in that: The method for establishing the runway type identification network includes: Get different runway types and corresponding braking status data; The brake status data in the training data set is used as input, and the corresponding runway type is used as output, and a support vector machine is trained to obtain a runway type recognition network.

6. The method for estimating the combined characteristics of a track and a tire according to claim 1, characterized in that: The state information of the anti-skid braking system includes aircraft speed and brake pressure; and establishing a runway / tire combination characteristic model according to the state information of the anti-skid braking system of the current flight includes: determining a braking torque according to the braking pressure; Establishing a wheel dynamics model according to the braking torque, wheel angular acceleration, and runway / tire combination coefficient; Establishing a slip model according to the wheel angular acceleration, aircraft speed and slip rate; Establishing a friction model according to the slip ratio and the track / tire combination coefficient; A runway / tire combination characteristic model is established according to the wheel dynamics model, the slip model and the friction model.

7. The method for estimating the combined characteristics of a track and a tire according to claim 1 or 6, characterized in that: The track / tire combination characteristic model is: Where k is the moment, x = [BCD], B is the stiffness factor, C is the shape factor, and D is the peak factor. is the wheel angular acceleration, s is the slip rate, μ is the runway / tire combination coefficient, w k and v k are system noise and measurement noise, respectively.

8. The method for estimating the combined characteristics of a track and a tire according to claim 7, characterized in that: The wheel dynamics model is: In the formula, F N is the wheel load, r m is the wheel rolling radius, k b is the brake pressure-brake torque ratio coefficient, P b is the brake pressure, J w is the wheel moment of inertia.

9. The method for estimating the combined characteristics of a track and a tire according to claim 8, characterized in that: The slip model is: Where V p represents the aircraft speed, ω w is the wheel angular velocity.

10. The method for estimating the combined characteristics of a track and a tire according to claim 9, characterized in that: The friction model is: μ=D·sin(C·arctan(Bs)).

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