Unmanned aerial vehicle steering effect analysis method based on wind disturbance

Abstract

The invention discloses an unmanned aerial vehicle steering effect analysis method based on wind disturbance, and belongs to the technical field of unmanned aerial vehicle overall design and flight control. Comprising the following steps: 1, constructing a coupling simulation model; step 1.1, constructing a six-degree-of-freedom rigid body dynamic model; step 1.2, integrating control laws; step 2, dynamically injecting a wind disturbance scene; step 3, dynamically evaluating the steering effect and extracting parameters; step 4, carrying out multidisciplinary collaborative optimization; and 5, carrying out closed-loop iteration verification. Dynamic closed-loop test: breaking through traditional static analysis, realizing real-time interactive simulation of wind disturbance-control-steering effect, and accurately capturing nonlinear transient response; multi-source disturbance fusion: discrete gust and continuous turbulence are compositely injected, so that challenges of complex meteorological conditions on steerage are reflected more truly; and cross-domain parameter linkage: through dynamic iteration of pneumatic, control and structural parameters, system performance loss caused by excessive design of a single specialty is avoided.

Description

Technical Field

[0001] This invention belongs to the field of UAV overall design and flight control technology, specifically a rudder efficiency analysis method that combines aerodynamic characteristics, dynamic modeling, and flight control laws. Its core goal is to dynamically evaluate the responsiveness of the UAV's rudder surfaces under strong wind disturbances, providing a basis for optimizing rudder parameters during the overall design phase and balancing the design conflicts between flight stability, structural weight, and rudder performance. Background Art

[0002] Currently, UAV flight control in complex airflow environments faces severe challenges, especially when encountering strong gusts of wind at the left boundary of the flight envelope. The rudders need to deflect at high frequencies and large angles to maintain stability. This usage scenario requires higher requirements for rudder efficiency design.

[0003] Traditional design methods often rely on experience or conservative parameters, such as increasing the size of the rudders and boosting the power of the servos to cover extreme operating conditions. While this approach can reduce the risk of loss of control, it leads to increased weight, increased energy consumption, and even shortened service life due to excessive servo loads. Furthermore, existing technologies often separate the coupling between aerodynamic characteristics, dynamic response, and control law design. Estimating rudder effectiveness solely through static aerodynamic data or simplified models makes it difficult to accurately assess the true needs of the rudders under dynamic wind disturbances. This design deviation can lead to two extremes: overdesign resulting in a waste of resources, or insufficient rudder effectiveness leading to flight loss of control. Scientifically quantifying the anti-disturbance capability of rudders without sacrificing lightweighting and reliability has become a bottleneck that urgently needs to be overcome in UAV design. Summary of the Invention

[0004] To address the shortcomings of traditional design methods, this paper proposes a rudder efficiency analysis framework that couples dynamics and control laws. Through dynamic simulation and multidisciplinary collaborative optimization, it accurately quantifies the rudder surface requirements of drones under extreme wind disturbances. The core concept is to break through the disconnected nature of aerodynamics, control, and structural design, deeply integrating drone dynamics and control law models with real-world wind disturbance scenarios to create a closed-loop "disturbance-response-decision" evaluation system.

[0005] Specifically, the method first establishes a six-degree-of-freedom UAV dynamics model and embeds the actual flight control laws to create a simulation environment capable of simulating the real-time deflection of rudder surfaces and aerodynamic coupling effects. Furthermore, wind disturbances consistent with actual weather conditions are injected for high-risk conditions, such as the left edge of the flight envelope. High-precision simulations are then used to capture the dynamic responses of rudder deflection angle and rate.

[0006] Unlike traditional static analysis, this method specifically focuses on the dynamic interaction between control laws and rudder deflection. For example, when strong wind disturbances cause attitude deviation, the command signal generated by the control law will drive the rudder to deflect rapidly. However, the size, inertia, and servo bandwidth of the rudder itself will in turn affect the stability of the closed-loop control. Through repeated iterative simulations, key parameters such as the maximum deflection angle and rudder deflection rate of the rudder under extreme operating conditions can be extracted, providing a quantitative basis for UAV design. If the rudder efficiency margin is insufficient, the aerodynamic layout can be optimized or the rudder size can be adjusted. If the servo load exceeds the limit, the servo power can be re-matched or the control algorithm bandwidth can be optimized.

[0007] This method, for the first time, transforms "wind resistance" into a quantifiable design indicator, avoiding the redundant weight caused by over-reliance on safety margins and ensuring the matching of rudder effectiveness and control through dynamic coupling analysis, ultimately achieving global optimization of flight stability, structural lightweighting, and servo performance.

[0008] The core of this invention is to construct a closed-loop technical process of "modeling-injection-evaluation-optimization", which realizes the accurate quantification and design optimization of rudder efficiency requirements through high-precision dynamic simulation and multidisciplinary parameter linkage.

[0009] The technical solution of the present invention:

[0010] A method for analyzing the rudder efficiency of a UAV based on wind disturbance is proposed. The specific steps are as follows:

[0011] Step 1: Coupled simulation model construction

[0012] Step 1.1: Construction of six-degree-of-freedom rigid body dynamics model

[0013] Based on the UAV's geometric parameter data, mass distribution characteristic data (weight center of gravity, moment of inertia), aerodynamic characteristic data and engine thrust data, a six-degree-of-freedom rigid body dynamics model of the UAV is constructed. The six-degree-of-freedom rigid body dynamics model of the UAV covers aerodynamic force / torque calculation and body dynamics / kinematics calculation.

[0014] Step 1.2: Control law integration

[0015] The actual flight control law is embedded in the six-degree-of-freedom rigid body dynamics model to form a simulation environment with bidirectional coupling of "dynamics-control law"; this simulation environment can simulate the dynamic response of the rudder under the combined action of control instructions and wind disturbance.

[0016] Step 2: Dynamic injection of wind disturbance scene

[0017] In the left boundary flight state of the UAV, discrete gusts and continuous turbulence are applied to simulate the multi-dimensional wind disturbance test scenario.

[0018] Discrete gust: Define the tri-axial gust amplitude and wind field length according to MIL-F-8785C standard or measured data to simulate gust impacts;

[0019] Continuous turbulence: Generate a three-dimensional turbulence field using the Dryden or Von Kármán spectrum and input it into the coupled simulation model constructed in step 1 in real time in the time domain. The coupled simulation model is then run in the simulation platform to drive the simulation platform to simulate the dynamic response process of the UAV under disturbance.

[0020] Step 3: Dynamic evaluation of rudder efficiency and parameter extraction

[0021] In the closed-loop simulation with wind disturbance applied, two key values, the rudder deflection angle and the rudder surface deflection rate, are recorded in real time, and their correlation with the wind disturbance intensity is analyzed.

[0022] Step 4: Multidisciplinary Collaborative Optimization

[0023] Feedback the required rudder deflection angle and rudder surface deflection rate under wind disturbance to the aerodynamics and flight control disciplines, forming a cross-disciplinary optimization logic:

[0024] If the maximum transient rudder deflection angle approaches the physical limit of the rudder surface, give priority to adjusting the aerodynamic layout (such as increasing the rudder surface area and optimizing the airfoil parameters) to increase the aerodynamic torque and reduce dependence on the rudder effect;

[0025] If the rudder deflection rate exceeds the limit, the joint control professional optimizes the control law bandwidth or reselects a high-rate response servo;

[0026] Step 5: Closed-loop iterative verification

[0027] Based on the feedback, the aerodynamic layout was revised and a new round of geometric parameter data, mass distribution characteristics (weight center of gravity, moment of inertia), and aerodynamic characteristics data were calculated. The flight dynamics model was updated and wind disturbance simulations were repeated until the rudder effectiveness margin (the difference between the physical rudder deflection limit and the actual required rudder deflection) met the safety threshold and the rudder deflection rate was within a reasonable range. The final output was a balanced design that took into account wind disturbance resistance, structural weight, and rudder performance.

[0028] Beneficial effects of the present invention:

[0029] Dynamic closed-loop testing: Breaking through traditional static analysis, it achieves real-time interactive simulation of wind disturbance, control, and steering efficiency, accurately capturing nonlinear transient responses.

[0030] Multi-source disturbance fusion: composite injection of discrete gusts and continuous turbulence to more realistically reflect the challenges posed by complex weather conditions to steering efficiency;

[0031] Cross-domain parameter linkage: Through dynamic iteration of aerodynamic, control, and structural parameters, system performance loss caused by over-design of a single discipline can be avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The technical solution of the present invention is visually explained below with reference to the accompanying drawings. The components or processes in the drawings correspond to the descriptions in the specific implementation methods, which are used to assist in understanding the core design logic and implementation steps of the present invention.

[0033] Figure 1 This is the architecture diagram of the rudder efficiency analysis system.

[0034] Figure 2 Schematic diagram of wind disturbance injection.

[0035] Figure 3 Schematic diagram of multidisciplinary parameter linkage optimization.

[0036] Figure 4 This is the rudder deflection response curve under wind disturbance.

[0037] Figure 5 This is the rudder rate response curve under wind disturbance. DETAILED DESCRIPTION

[0038] The implementation process of the present invention is carried out around the analysis of the rudder efficiency of UAV based on wind disturbance:

[0039] A wind disturbance-based UAV steering efficiency analysis system includes an evaluation module, a dynamics module, a control law module, and a wind injection module. The dynamics module is used to construct a six-degree-of-freedom rigid body dynamics model; the control law module is used for control law integration; the wind injection module is used for wind disturbance scenario configuration and injection; and the evaluation module is used for dynamic simulation and steering efficiency evaluation, as well as parameter optimization and model optimization. The specific implementation is as follows:

[0040] Step 1: Coupled simulation model construction

[0041] Step 1.1: Construction of six-degree-of-freedom rigid body dynamics model

[0042] Based on the UAV's geometric parameter data, mass distribution characteristics (weight center of gravity, moment of inertia) data, aerodynamic characteristics data and engine thrust data, a rigid body dynamics model with six degrees of freedom is established based on the Newton-Euler equation.

[0043] Step 1.2: Control law integration

[0044] The actual flight control law is embedded in the six-degree-of-freedom rigid body dynamics model to form a simulation environment with a bidirectional coupling of "dynamics-control law". This simulation environment is used to simulate the dynamic response of the rudder under the combined action of control commands and wind disturbances. Taking pitch angle control as an example, the control law calculates the difference between the pitch angle command and the pitch angle response, and then calculates the output rudder deflection command through the pitch control law, and simulates the actual rudder dynamic response through the servo model (second-order lag link).

[0045] Step 2: Configure and inject wind disturbance scene

[0046] Step 2.1, define the left boundary flight state

[0047] Place the drone in low-speed level flight and ensure it flies straight and level on the left side of the flight envelope.

[0048] Step 2.2: Composite wind disturbance generation

[0049] Discrete gust: According to MIL-F-8785C standard, set the triaxial gust intensity and wind field length, as well as the action time; according to the requirements of the national military standard, the gust waveform adopts the "1-cos" type;

[0050] Continuous turbulence: Generate a three-dimensional turbulence field using the Dryden spectrum, set the turbulence scale length, and turbulence severity parameters.

[0051] The wind disturbance data assigned in the ground coordinate system are converted into velocity disturbance components in the aircraft coordinate system and superimposed into the flight dynamics model in real time.

[0052] Step 3: Dynamic simulation and rudder efficiency evaluation

[0053] 3.1. Run the closed-loop model

[0054] Run the "dynamics-control law" coupled model in the Matlab / Simulink simulation platform. The simulation duration should cover the entire wind disturbance cycle. Record the time domain response of the rudder angle and rudder rate parameters.

[0055] 3.2. Key indicator extraction

[0056] The rudder deflection angle and rudder deflection rate values ​​in the simulation are extracted and compared with the physical deflection angle limit and rudder deflection rate limit of the servo to calculate the rudder efficiency margin.

[0057] 4. Parameter Optimization

[0058] 4.1. Aerodynamic layout adjustment

[0059] If the rudder efficiency margin is lower than the set threshold range, the aerodynamic design is optimized first:

[0060] Increase the rudder surface area: increase the turning torque generated by unit rudder deflection angle;

[0061] Adjust the airfoil shape: Adjust the airfoil camber, twist angle, thickness and other parameters to reduce the static stability of the drone, keep the drone in a neutral and stable state as much as possible, and reduce dependence on the rudder effect.

[0062] If the rudder effectiveness margin is higher than the set threshold range, the rudder surface area is reduced to reduce the burden on the aerodynamic layout and structural strength.

[0063] 5. Closed-loop verification and convergence

[0064] The optimization suggestions are fed back to the aerodynamics and flight control departments, the new round of adjusted parameters are updated to the coupling model, and the same wind disturbance is re-injected for simulation.

[0065] If the standard is not met, repeat steps 3 and 4 until all indicators converge to the safety threshold.

Claims

1. A method for analyzing the rudder efficiency of an unmanned aerial vehicle based on wind disturbance, characterized in that: The specific steps are as follows: Step 1: Construction of coupled simulation model; Step 1.1, construct a six-degree-of-freedom rigid body dynamics model; Step 1.2, control law integration; Step 2: Dynamic injection of wind disturbance scene; Step 3: Dynamic evaluation of rudder efficiency and parameter extraction; Step 4: Multidisciplinary collaborative optimization; Step 5: Closed-loop iterative verification.

2. The method for analyzing the rudder efficiency of an unmanned aerial vehicle based on wind disturbance according to claim 1, characterized in that: Step 1.1 is as follows: Based on the UAV's geometric parameter data, mass distribution characteristic data, aerodynamic characteristic data, and engine thrust data, a six-degree-of-freedom rigid body dynamics model of the UAV is constructed. The six-degree-of-freedom rigid body dynamics model of the UAV covers aerodynamic force / torque calculations and body dynamics / kinematic calculations; the mass distribution characteristic data includes weight center of gravity and moment of inertia.

3. The method for analyzing the rudder efficiency of an unmanned aerial vehicle based on wind disturbance according to claim 1, characterized in that: Step 1.2 specifically involves embedding the actual flight control law into a six-degree-of-freedom rigid-body dynamics model to create a simulation environment with a bidirectional coupling between dynamics and control law. This simulation environment can simulate the dynamic response of the control surfaces under the combined effects of control commands and wind disturbances.

4. The method for analyzing the rudder efficiency of an unmanned aerial vehicle based on wind disturbance according to claim 1, characterized in that: Step 2 is as follows: in the UAV left boundary flight state, discrete gusts and continuous turbulence are applied to simulate the multi-dimensional wind disturbance test scenario; Discrete gust: Define the amplitude and wind field length of the three-axis gust according to the MIL-F-8785C standard or measured data to simulate the impact of the gust. Continuous turbulence: Generate a three-dimensional turbulence field using the Dryden or Von Kármán spectrum and input it into the coupled simulation model constructed in step 1 in real time in the time domain. Then, run the coupled simulation model in the simulation platform to drive the simulation platform to simulate the dynamic response process of the UAV under disturbance.

5. The method for analyzing the rudder efficiency of an unmanned aerial vehicle based on wind disturbance according to claim 1, characterized in that: Step 3 is as follows: in the closed-loop simulation with wind disturbance, two key values, namely the rudder deflection angle and the rudder surface deflection rate, are recorded in real time, and their correlation with the wind disturbance intensity is analyzed.

6. The method for analyzing the rudder efficiency of an unmanned aerial vehicle based on wind disturbance according to claim 1, characterized in that: Step 4 is as follows: Feedback the required rudder deflection angle and the required rudder surface deflection rate under wind disturbance to the aerodynamics and flight control disciplines, forming a cross-disciplinary optimization logic: If the maximum transient rudder deflection angle approaches the physical limit of the rudder surface, give priority to adjusting the aerodynamic layout (such as increasing the rudder surface area and optimizing the airfoil parameters) to increase the aerodynamic torque and reduce dependence on the rudder effect; If the rudder deflection rate exceeds the limit, the joint control professional optimizes the control law bandwidth or reselects a high-rate response servo.

7. The method for analyzing the rudder efficiency of an unmanned aerial vehicle based on wind disturbance according to claim 1, characterized in that: Step 5 is as follows: based on the feedback, the aerodynamic layout is modified and a new round of geometric parameter data, mass distribution characteristic data, and aerodynamic characteristic data are calculated. The flight dynamics model is updated and wind disturbance simulation is repeated until the rudder effectiveness margin meets the safety threshold and the rudder surface deflection rate is within a reasonable range. Finally, a balanced design scheme that takes into account wind disturbance resistance, structural weight, and servo performance is output.

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