Aircraft falling speed control method and device based on error dynamics and medium
Through the aircraft falling speed control method based on error dynamics, using the extended state observer and proportional guidance law, the robustness and energy efficiency problems of aircraft falling speed control under nonlinearity and uncertainty are solved, and high-precision falling speed control is achieved.
Patent Information
- Application Number
- CN202510473171.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-16
- Publication Date
- 2025-09-05
AI Technical Summary
Existing aircraft fall velocity control methods have difficulty achieving high-precision, robust, and energy-efficient fall velocity control when faced with nonlinear, strongly underactuated, and finite-time characteristics, especially when their performance degrades under parameter and environmental uncertainties.
A control method based on error dynamics is adopted, and the extended state observer is used to obtain the drag in real time. Combined with the proportional guidance law and energy optimal guidance, guidance instructions are generated through fixed-time convergence error dynamics to construct an aircraft fall speed control method, including building a relative kinematic model of aircraft guidance, establishing the relationship between fall speed and remaining flight distance, correcting the drag acceleration in real time, introducing fixed-time convergence error dynamics equations and generating guidance instructions.
It achieves fixed-time convergence in the face of parameter and environmental uncertainties, improves the robustness and precision of falling speed control, avoids excessive energy consumption, and enhances anti-interference ability.
Smart Images
Figure CN120595571A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of aircraft fall speed constraint guidance technology, and in particular to an aircraft fall speed control method, device and medium based on error dynamics. Background Art The problem of aircraft fall velocity constraint guidance is characterized by nonlinearity, strong underactuation, and finite time. Unlike conventional constraints such as landing point and landing angle, the control of fall velocity more clearly exhibits a "one-way adjustment" characteristic. For example, for an unpowered glider, significant deceleration can be achieved through aircraft maneuvers, but acceleration regulation is otherwise very limited. The strong correlation between fall velocity and aircraft dynamics further increases the complexity of the task. Existing multi-constraint guidance methods can be divided into non-optimal guidance methods and optimal guidance methods. The former is based on deterministic environmental parameters and dynamic models, and controls the fall velocity by tracking the flight velocity curve, placing high demands on the performance and robustness of the tracking control loop. The latter uses optimization methods to design the aircraft trajectory under multiple constraints to directly obtain the control sequence. However, the uncertainty of parameters and environment, as well as the high nonlinearity of the dynamics, severely compresses the feasible domain of the flight trajectory, placing extremely high demands on the convergence and online real-time computing capabilities of the optimization algorithm. Summary of the Invention
[0002] In view of this, the present invention provides an aircraft fall speed control method, device and medium based on error dynamics, which can realize closed-loop adaptive fall speed constraint guidance with finite time convergence and improve the robustness of the fall speed constraint guidance.
[0003] To achieve the above purpose, the technical solution of the present invention is as follows:
[0004] A method for controlling the falling speed of an aircraft based on error dynamics is proposed. The relationship between the aircraft maneuverability and the falling speed is established using the remaining flight distance, and the fixed-time convergence error dynamics is established to obtain the guidance instructions so that the falling speed error converges within a fixed time. The method uses an extended state observer to obtain real-time resistance, a proportional guidance law to predict the falling speed of the aircraft, and fixed-time convergence error dynamics to generate guidance instructions.
[0005] The steps include:
[0006] Step 1: Construct a mathematical description of the aircraft's falling velocity constraint guidance problem, including establishing a relative kinematic model and constraints for the aircraft's guidance. The model is a differential equation that includes the distance R between the aircraft and the target, the lead angle σ, the velocity direction angle θ, the line of sight angle q, the aircraft's velocity V, and initial and terminal constraints.
[0007] Step 2: Establish the relationship between the falling speed and the remaining flight distance, and use the remaining flight time t go and drag acceleration a xThe integral calculation predicts the falling speed V f ;
[0008] Step 3: Construct an extended state observer to estimate the uncertainty δ of the drag acceleration in real time and calculate it through the observer equation Corrected resistance prediction value; where F(X) is a nonlinear function of the state, B = [0, 0, -1 / V k ,0] T , V k is the current flight speed, u is the system input; L is the parameter matrix of the state observer, e is the control error of the control system; the superscript “.” indicates the derivative;
[0009] Step 4: Introduce the error dynamics equation with fixed time convergence Among them, the control error e is the expected falling speed V d With proportional guidance, the predicted falling speed V fpre The difference; k is the calculation parameter;
[0010] Step 5: Generate guidance commands based on the biased proportional guidance law Combined with the corrected bias term f(σ) is a continuous function of the lead angle σ; N is the navigation ratio of the proportional guidance law;
[0011] Step 6: Apply guidance commands to the aircraft and update the state in a closed loop until the target is reached.
[0012] Among them, in the step 2, the resistance acceleration a is corrected in real time x .
[0013] Among them, the analytical solution of the control error is:
[0014] Where e0 is the control error at the initial moment, t f is the terminal moment, t is the current moment, and k>0, ensuring that the control error converges to zero when the remaining flight time approaches zero.
[0015] Among them, the continuous function of the lead angle σ is:
[0016]
[0017] Among them, η>1, σ m To set the threshold.
[0018] Among them, in the step 2, the remaining flight distance is calculated using numerical integration or neural network prediction method, and is dynamically updated in combination with the time-varying characteristics of the drag acceleration.
[0019] The present invention also provides an electronic device, which includes a processor and a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement the aircraft fall speed control method based on error dynamics described in the present invention.
[0020] The present invention also provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the aircraft falling speed control method based on error dynamics described in the present invention.
[0021] Beneficial effects:
[0022] 1. The method of the present invention is a closed-loop adaptive falling speed constraint guidance method that includes fixed-time convergence error dynamics theory, optimal falling angle constraint proportional guidance, and extended state observer, which considers the uncertainty of aircraft dynamics and environmental uncertainty under terminal multi-constraints. Compared with the classical proportional guidance law and the method of speed correction based on tracking the flight speed curve, the extended state observer is introduced in the guidance law design to obtain real-time resistance. Combined with the energy optimal guidance method, it avoids excessive flight energy consumption caused by parameter and environmental uncertainty, and ensures the convergence of falling speed error through error dynamics analysis, effectively improving the robustness of falling speed control, and enhancing the guidance accuracy and anti-interference ability.
[0023] 2. The method of the present invention is aimed at the requirements of aircraft fall speed constraint guidance for high guidance precision, small fall speed error, fixed time convergence and strong robustness, and proposes a fall speed constraint guidance law design method based on error dynamics analysis, combined with an extended state observer to solve the adverse effects of parameter and environmental uncertainty on fall speed control, and on the basis of the classical proportional guidance law, analyzes the relationship between aircraft maneuverability and fall speed, thereby constructing a predicted fall speed feedback, and proposes a design method for improving the proportional guidance law bias term, generates a guidance instruction with fixed time convergence of fall speed error, and forms a complete set of fall speed constraint guidance law design methods based on error dynamics theory and an extended state observer.
[0024] 3. In the method of the present invention, the influence of uncertainty on the terminal state is predicted by constructing an extended state observer, so that the aircraft's landing speed control is insensitive to the uncertainty of parameters and environment; based on the analysis of the classical proportional guidance law, the relationship between aircraft maneuverability and landing speed is established using the remaining flight distance, and the fixed-time convergence error dynamics is established to obtain the guidance instruction, so that the landing speed error converges within a fixed time, and energy is saved to deal with aerodynamic uncertainty, thereby improving the robustness of the landing speed control.
[0025] 4. In the method of the present invention, in order to avoid the singularity problem caused by the lead angle σ=0 in the denominator of the bias term in practical applications and to ensure the continuity of the guidance instructions, an auxiliary function f(σ) is introduced to correct the bias term.
[0026] 5. The device of the present invention is used to implement the method of the present invention. An extended state observer is introduced into the guidance law design to obtain real-time resistance. Combined with the energy optimal guidance method, excessive flight energy consumption caused by parameter and environmental uncertainties is avoided. The convergence of the falling speed error is ensured through error dynamics analysis, which effectively improves the robustness of the falling speed control and enhances the guidance accuracy and anti-interference capability.
[0027] 6. The medium of the present invention is used to implement the method of the present invention. An extended state observer is introduced into the guidance law design to obtain real-time resistance. Combined with the energy optimal guidance method, excessive flight energy consumption caused by parameter and environmental uncertainties is avoided. The convergence of the falling speed error is ensured through error dynamics analysis, which effectively improves the robustness of the falling speed control and enhances the guidance accuracy and anti-interference capability. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] Figure 1 This is a flow chart of a method according to an embodiment of the present invention.
[0029] Figure 2 This is an example of the relative kinematic geometric relationship of aircraft guidance in the method of the present invention.
[0030] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0031] The present invention is described in detail below with reference to the accompanying drawings and embodiments.
[0032] The present invention is aimed at the requirements of high-precision guidance, small error in falling speed, fixed-time convergence and strong robustness for aircraft falling speed constraint guidance. Combining the energy-saving effect of the optimal guidance method and using the extended state observer to predict the effect of drag acceleration uncertainty on the guidance terminal state, the closed-loop adaptive falling speed constraint guidance method with finite time convergence is studied to improve the robustness of falling speed constraint guidance. Specifically, a falling speed constraint guidance method is proposed based on the extended state observer and the fixed-time convergence error dynamics theory. By constructing an extended state observer to predict the impact of uncertainty on the terminal state, the aircraft falling speed control is made insensitive to the uncertainty of parameters and environment. Based on the analysis of the classical proportional guidance law, the relationship between aircraft maneuverability and falling speed is established using the remaining flight distance. The fixed-time convergence error dynamics is established to obtain the guidance instruction, so that the falling speed error converges within a fixed time, saves energy to deal with aerodynamic uncertainty, and improves the robustness of falling speed control. The key technologies include: real-time drag acquisition based on the extended state observer, aircraft falling speed prediction based on the proportional guidance law, and guidance instruction generation based on the fixed-time convergence error dynamics.
[0033] The present invention provides a method for controlling the falling speed of an aircraft based on error dynamics, which is specifically implemented as follows: Figure 1 , including the following steps:
[0034] Step 1: Based on the specific mission requirements, construct a mathematical description of the aircraft's falling speed constraint guidance problem, including establishing the aircraft's guidance relative kinematic model and constraint conditions.
[0035] The relative kinematic model of aircraft guidance can be described as follows:
[0036]
[0037] Where: R is the distance between the aircraft and the target, σ is the angle between the line connecting the aircraft and the target (i.e., the line of sight) and the direction of the aircraft's velocity (i.e., the lead angle), θ is the angle between the direction of the aircraft's velocity and the horizontal plane (i.e., the velocity direction angle), and q is the angle between the line of sight and the horizontal plane (i.e., the line of sight angle). All the above angles are positive in the counterclockwise direction. y is the normal acceleration of the aircraft, V is the aircraft velocity, G is the gravity acting on the aircraft, D is the drag acting on the aircraft, and m is the aircraft mass. The superscript "." indicates the derivative. To facilitate subsequent design, the normal force and gravity are combined to express the following combined normal acceleration:
[0038]
[0039] In subsequent designs, the accelerations used are all the combined normal accelerations defined by equation (5).
[0040] The relative kinematic geometric relationship of aircraft guidance is as follows Figure 2 shown.
[0041] The geometric relationships between the angles are as follows:
[0042] σ=θ-q (6)
[0043] Therefore, the differential equation for the lead angle σ is:
[0044]
[0045] The initial and terminal constraints of the aircraft falling speed constraint guidance problem are described as follows:
[0046] q(t0)=q0, R(t0)=R0, θ(t0)=θ0, σ(t0)=σ0, (8)
[0047] q(t f )=θ(t f ),R(t f )=0,σ(t f )=0,V(t f )=Vd (9)
[0048] In the formula, the quantity with subscript 0 represents the initial state of the aircraft, t0 is the initial time, t f is the terminal moment, V d is the design target falling speed.
[0049] In addition, in order to minimize the actual miss distance and satisfy the terminal acceleration constraint, it is usually desired that the terminal acceleration be zero, i.e., a f =0.
[0050] Step 2: Establish the relationship between the falling speed and the remaining flight distance.
[0051] The drag acceleration is constant at a x When the remaining flight distance L go It is obtained from the following formula:
[0052]
[0053] Where, t go is the remaining flight time, V k is the current flight speed.
[0054] The time derivative of the falling velocity can be written as:
[0055]
[0056] In actual flight, the drag acceleration depends on the current state and control input, and the remaining flight distance and fall speed can be obtained:
[0057]
[0058] Where X = [R, q, σ, V] T is the aircraft state vector. Since the drag acceleration is time-varying and the remaining flight time is unknown, Equation (13) cannot be solved analytically. Instead, a numerical integration method can be used. Given a given flight path, the remaining flight distance is not affected by the flight speed, so the time derivative of the aircraft state X is:
[0059]
[0060] In the formula, u=a PN is the system input, a PN is the guidance overload command given by the proportional guidance law. Thus, after giving the initial value of differential equation (14), numerical integration can be performed to obtain the reference trajectory from the current position of the aircraft to the target position, and the remaining flight time and predicted fall speed of the reference trajectory can also be calculated. Alternatively, an offline dataset can be generated and the fall speed can be predicted online based on the trained neural network.
[0061] Step 3: Establish a state observer for the aircraft's drag acceleration.
[0062] Considering that the drag acceleration has uncertainty δ, which is often an additive deviation, equations (12) and (13) should be:
[0063]
[0064] Obviously, if the remaining flight distance remains constant, drag acceleration will affect the remaining flight time and, consequently, the descent velocity. When the actual drag is less than the ideal drag, the aircraft still has some energy to correct the descent velocity through maneuvers. However, when the actual drag is greater than the ideal drag, the aircraft consumes too much kinetic energy during the initial flight trajectory, making it difficult to achieve the ideal descent velocity by the end of the guidance phase. Therefore, a disturbance observer is introduced into the descent velocity prediction.
[0065] The relative kinematic model of the aforementioned aircraft guidance can be written as follows:
[0066]
[0067] Where F(X) is a nonlinear function of the state, B = [0, 0, -1 / V k ,0] T , y is the constraint state considered by the present invention, i.e., velocity, D = [0, 0, 0, 1] T .
[0068] Based on this model, the disturbance observer is written as:
[0069]
[0070] Where L is the parameter matrix of the state observer, which can be designed based on the specific performance of the observer. Clearly, this state observer satisfies the condition that when Z → X, e → 0. Introducing this disturbance observer allows for more accurate drag acceleration in real time, improving the accuracy of fall velocity prediction.
[0071] Step 4: Introduce error dynamics that specify the guidance law control error convergence performance.
[0072] The error dynamics take the form:
[0073]
[0074] Where e is the control error of the control system, and e0 is the control error at the initial moment. The analytical solution of formula (19) is:
[0075]
[0076] It can be seen that when the parameter k>0, when the remaining aircraft time tends to zero, the control error also converges to zero.
[0077] Step five: Calculate the guidance instructions for the aircraft.
[0078] Adopt bias proportional guidance law with speed error feedback term:
[0079]
[0080] Where N is the navigation ratio of the proportional guidance law, which is required to be greater than 2; a s It is the guidance instruction offset item.
[0081] The time derivative of the lead angle under this guidance law is:
[0082]
[0083] Considering that the actual leading angle is mostly small, the remaining flight distance under this guidance law is
[0084]
[0085] Taking the derivative of the above formula with respect to time, we get:
[0086]
[0087] Define the control error as the expected falling speed V d Predicted falling speed with proportional guidance Difference:
[0088]
[0089] The time derivative of the control error is:
[0090]
[0091] From the above formula, the guidance command bias term of the error dynamics that satisfies formula (19) is:
[0092]
[0093] In order to avoid the singularity problem caused by the lead angle σ=0 in the denominator of the bias term in practical applications and ensure the continuity of the guidance command, an auxiliary function f(σ) is introduced to correct the bias term:
[0094]
[0095] Where f(σ) is a continuous function of the lead angle σ:
[0096]
[0097] Among them, η>1, σ m You can choose according to your needs.
[0098] Substituting the bias term back into equation (21), the guidance command is
[0099]
[0100] Step 6: Apply the guidance command obtained above to the aircraft, and after updating the current state of the aircraft in the next guidance cycle, return to step 2 until the aircraft reaches the target.
[0101] The embodiment of the present application also provides an electronic device, Figure 3 The structure of an electronic device provided by an embodiment of the present invention is shown. For example, the electronic device 30 may include a processor 31, a memory 32, and a transmission device 33. The processor 31 is used to execute the text error correction method based on the occlusion language model mentioned in the above embodiment, wherein the processor and the memory may be connected via a bus or other means, taking a bus connection as an example. The transmission device may be connected to the processor and the memory via a wired or wireless manner. The memory, as a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as the program instructions / modules corresponding to the text error correction method based on the occlusion language model in the embodiment of the present application. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory, that is, implementing the text error correction method based on the occlusion language model in the above method embodiment. The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the aforementioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. The one or more modules are stored in the memory and, when executed by the processor, perform the text error correction method based on the occlusion language model in the embodiment.
[0102] As another aspect, the present application also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the device described in the above embodiment; or it may be a computer-readable storage medium that exists independently and is not assembled into the device. The computer-readable storage medium may be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute the text error correction method based on the occlusion language model described in the present application.
[0103] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A method for controlling the falling speed of an aircraft based on error dynamics, characterized in that: The relationship between aircraft maneuverability and fall speed is established using the remaining flight distance, and the fixed-time convergence error dynamics is established to obtain guidance instructions, so that the fall speed error converges within a fixed time. The real-time drag is acquired based on the expanded state observer, the aircraft fall speed is predicted based on the proportional guidance law, and the guidance instructions are generated based on the fixed-time convergence error dynamics.
2. The method according to claim 1, wherein The following steps are involved: Step 1: Construct a mathematical description of the aircraft's falling velocity constraint guidance problem, including establishing a relative kinematic model and constraints for the aircraft's guidance. The model is a differential equation that includes the distance R between the aircraft and the target, the lead angle σ, the velocity direction angle θ, the line of sight angle q, the aircraft's velocity V, and initial and terminal constraints. Step 2: Establish the relationship between the falling speed and the remaining flight distance, and use the remaining flight time t go and drag acceleration a x The integral calculation predicts the falling speed V f ; Step 3: Construct an extended state observer to estimate the uncertainty δ of the drag acceleration in real time and calculate it through the observer equation Corrected resistance prediction value; where F(X) is a nonlinear function of the state, B = [0, 0, -1 / V k ,0] T , V k is the current flight speed, u is the system input; L is the parameter matrix of the state observer, e is the control error of the control system; the superscript ". " indicates the derivative; Step 4: Introduce the error dynamics equation with fixed time convergence Among them, the control error e is the expected falling speed V d Predicted falling speed with proportional guidance The difference; k is the calculation parameter; Step 5: Generate guidance commands based on the biased proportional guidance law Combined with the corrected bias term f(σ) is a continuous function of the lead angle σ; N is the navigation ratio of the proportional guidance law; Step 6: Apply guidance commands to the aircraft and update the state in a closed loop until the target is reached.
3. The method according to claim 2, wherein In the step 2, the resistance acceleration a is corrected in real time. x .
4. The method according to claim 2 or 3, wherein: The analytical solution of the control error is: Where e0 is the control error at the initial moment, t f is the terminal moment, t is the current moment, and k>0, ensuring that the control error converges to zero when the remaining flight time approaches zero.
5. The method according to claim 4, wherein The continuous function of the lead angle σ is: Among them, η>1, σ m To set the threshold.
6. The method according to claim 2 or 3, wherein: In the second step, the remaining flight distance is calculated using numerical integration or neural network prediction methods, and is dynamically updated in combination with the time-varying characteristics of the drag acceleration.
7. An electronic device, characterized in that: The electronic device includes a processor and a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement the aircraft falling speed control method based on error dynamics as described in any one of claims 1 to 6.
8. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is used to execute the aircraft falling speed control method based on error dynamics as described in any one of claims 1 to 6.