CN-121855552-B - Intelligent generation method for reentry track of aircraft based on RBF neural network

Abstract

The invention belongs to the technical field of aircraft track optimization, and discloses an intelligent generation method of an aircraft reentry track based on an RBF neural network. According to the invention, firstly, an aircraft reentry model is constructed, a track optimization problem to be solved is constructed based on the model, an optimal sample track set is generated by using a pseudo-spectrum method according to initial discrete altitude speed, and then, a sample track is generated at each route point according to a new discrete state. And then dividing the sample set according to the waypoints to train RBF neural networks respectively, wherein the RBF neural networks are input into discrete high-speed states and output into a state-control sequence from the current state to the target point. And finally, using the RBF neural network to generate the optimal track on line. The simulation verification of the method has higher confidence coefficient and smaller error, and can greatly improve the generation efficiency of the optimal track.

Inventors

• YANG FENG
• CHENG ZIHENG
• ZHAO CHENGYU
• LIU KAI

Assignees

• 大连理工大学

Dates

Publication Date

20260508

Application Date

20260318

Claims (3)

1. 1. An intelligent generation method of an aircraft reentry track based on RBF neural network is characterized by comprising the following steps: step 1, building a reentry model; Step 2, constructing a track optimization problem; controlling the heat flux density, overload and dynamic pressure parameters in the reentry process to ensure that each index does not exceed a safety threshold; (4) (5) (6) Wherein: Respectively dynamic pressure, heat flux density and overload; respectively the maximum values of dynamic pressure, heat flux density and overload; For the sea level atmospheric density, For the first cosmic velocity, Is the radius of curvature of the head of the aircraft; Is an aircraft characteristic constant; reentry to the target longitude and latitude height, so the terminal constraint has strict position constraint, given by: (7) Wherein: The terminal time; For a time of The earth center distance during the time is equal to the earth center distance, For a time of Longitude of the time point, For a time of The latitude of the time at which the time is available, For the ground center distance of the terminal, For the longitude of the terminal, The latitude of the terminal; the control amount of the reentry process includes values of the attack angle and the roll angle, and in the process of solving by pseudo-spectrometry, the augmented attack angle and the roll angle are solved as states, and the change rates of the attack angle and the roll angle are solved as control amounts, so the control constraint is as follows: (8) Wherein: in order to achieve an angle of attack, In order for the rate of change of the angle of attack, Is the roll angle change rate; For the minimum and maximum value of the angle of attack, For the minimum and maximum value of the roll angle, For the minimum and maximum rate of change of angle of attack, Is the minimum and maximum value of the change rate of the roll angle; the minimum heating amount is selected as a performance index, namely: (9) Wherein: for the initial time period of time, the time period, The terminal time; selecting the minimum value of the integral value of the heat flux density from the initial time to the terminal time as a performance index; Step 3, generating an optimal track sample set; selecting a scattering state of height and speed at a route point to construct a track optimization problem considering uncertainty influence, and carrying out numerical solution by adopting a pseudo-spectrum method; step 4, training and applying the neural network; Forming a database by all flight trajectory data obtained by solving the trajectory optimization problem in the step 3, randomly extracting 70% of the total number of samples from the database as a training set, training an RBF neural network, taking the rest 30% of samples as test samples for checking the calculation precision of the neural network, respectively carrying out normalization processing on input data and output data in the training process, wherein the input of the RBF neural network is an initial height value and a speed value of each trajectory data, and outputting the initial height, speed, longitude and latitude, flight path angle, heading angle, attack angle and roll angle sequence data of each trajectory; The method comprises the steps of transferring a trained RBF neural network to online application, carrying out deviation judgment on the flight state of the RBF neural network at a waypoint to determine whether an optimal track needs to be updated, comparing the actual flight state with the determined track at the waypoint, and when any deviation value of the altitude or the speed in the flight state exceeds a set error, taking the current actual altitude and the speed as the input of the RBF neural network, and regenerating the tracked optimal track by using the RBF neural network, wherein the tracked optimal track comprises the altitude, the speed, the longitude and latitude, the flight path angle, the course angle, the attack angle and the roll angle.
2. 2. The intelligent generation method of the aircraft reentry track based on the RBF neural network as set forth in claim 1, wherein the step 1 is specifically as follows: step 1.1, reentry a dynamics model establishment; establishing an aircraft kinematics model according to a reentry kinematics principle: (1) Wherein, superscript' "Means the first derivative; the distance from the aircraft to the earth's center is expressed as the earth's center distance, As the longitude of the point of projection of the aircraft on the ground, The latitude of the projection point of the aircraft on the ground surface is given; In order to be able to achieve a speed, In order to achieve the angle of the flight path, Is a course angle; As a lifting force, the lift force is, Is resistance; for the mass of the aircraft it is, For the acceleration of the earth's gravity, In order to be at a roll angle, Indicating the rotation angular velocity; Step 1.2, establishing a pneumatic model; Including lift in an aircraft dynamics model Resistance force Expressed as: (2) (3) Wherein: The atmospheric density is a function of the height, and a fitting formula atmospheric model is adopted for calculation; In order to be able to achieve a speed, Is the aerodynamic area of the aircraft; respectively a lift coefficient and a drag coefficient as attack angles And Mach number Ma.
3. 3. The intelligent generation method of the aircraft reentry track based on the RBF neural network is characterized in that the solving process in the step 3 is characterized in that a pseudo-spectrum method is used for dispersing states and control variables in continuous time on high-order orthogonal distribution points such as Legendre-Gauss-Radau and the like, a global Lagrange interpolation polynomial is utilized for approximating the state track, so that a complex continuous optimal control problem is converted into a finite-dimensional nonlinear programming problem, a dynamics equation is converted into algebraic equation constraint on the distribution points, and a nonlinear programming solver is called for optimization calculation, so that a continuous flight track which meets constraint requirements under uncertainty conditions and enables performance indexes to be optimal is finally reconstructed, and track optimization is achieved.

Description

Intelligent generation method for reentry track of aircraft based on RBF neural network Technical Field The invention belongs to the field of aircraft track optimization, and relates to an intelligent generation method of an aircraft reentry track based on an RBF neural network. Background The hypersonic gliding aircraft has extremely high speed and range of motion capability, so that a long-distance hitting task can be performed, but the nominal track tracked in the guidance process can not meet the task requirement due to state deviation in the reentry process, so that the problem to be solved is that the nominal track is rapidly generated according to the real-time state in the reentry process of the hypersonic gliding aircraft. The prior art mainly has the following defects: At present, the generation of the reentry optimal track mainly depends on an off-line solving mode, such as a direct method, an indirect method and the like, namely, the track optimization problem is converted into an optimal control problem meeting a certain performance index to carry out solving calculation. In the use process, a large number of optimal track schemes are generated offline aiming at the state dispersion of the aircraft, adjacent reference track guidance tracking is selected according to the error state in real-time flight, and the calculation efficiency of the selection process is relatively low. The existing online optimal track generation method for engineering application is mainly a convex optimization method, wherein the convex optimization method is an optimization method in which an objective function is a convex function and a feasible domain set formed by constraint conditions is a convex set under the optimization requirement, and the online optimization method is used for carrying out online solution on the objective function in the online application process, has the problem of initial value sensitivity, and is long in solution calculation time and poor in calculation efficiency. With the rise of intelligent methods, research methods for online generation of tracks based on supervised learning exist, wherein control amounts are generated online according to a neural network, the generated tracks are calculated based on the control amounts, and the calculation efficiency is low. There is a need to develop a method to meet the requirement of online generation of an optimal track and improve the track generation efficiency. The prior related patent technology also has certain limitations: the patent 'a method for rapidly generating a finite time track of a hypersonic aircraft' (CN 103995540A) proposes a track generation method based on a convex optimization method. According to the method, the motion model of the reentry end track is subjected to optimization problem description, a nonlinear optimization problem is formed, the nonlinear optimization problem is subjected to salifying treatment, and the optimization index of the optimization problem and the constraint of the optimization problem are described as quadratic convex problems to solve. However, the method is easy to fall into local optimum when solving the optimum track, depends on the setting of an initial value, and may not have high calculation efficiency. The patent 'high-speed gliding type aircraft online track optimization method based on the deep neural network' (CN 118170155A) proposes an online track optimization method based on the deep neural network. The method inputs the state variable into a trained deep neural network to obtain a control variable of the aircraft, updates a roll angle and attack angle control instruction, and the aircraft flies according to the newly generated control instruction. However, when solving the optimal track, the method calculates the optimal track according to the control instruction value, so that the calculated amount is large, the calculation speed of the missile-borne computer is depended, and the efficiency is difficult to guarantee. In summary, the core problem of the prior art is that the calculation efficiency of the existing track generation method is difficult to be ensured because the optimal track is obtained through online calculation in the application process. Therefore, the research of the intelligent track generation algorithm is required to be carried out under the condition of state deviation. The intelligent track generation method can autonomously generate the flight path according to the real-time state data without carrying out a large amount of online calculation, has the capability of rapidly generating and adjusting the track, and ensures that the aircraft can rapidly generate the available nominal track under the condition of state deviation. Disclosure of Invention In order to solve the problems, the invention provides an intelligent generation method for the reentry track of the aircraft based on the RBF neural network, which realizes the online intelligent generation of the