Coaxial unmanned aerial vehicle variable speed and pitch control method and device, unmanned aerial vehicle and storage medium

Abstract

The application discloses a coaxial unmanned aerial vehicle variable speed and variable pitch control method and device, an unmanned aerial vehicle and a storage medium. The method comprises the following steps: acquiring current flight state data and target flight instructions of the unmanned aerial vehicle; determining the deviation of the target flight instructions and the flight state data, and calculating the total lift demand and the attitude control moment demand of the unmanned aerial vehicle based on the deviation; based on the total lift demand and the attitude control moment demand, the target rotating speed and the target pitch angle of the upper rotor and the lower rotor are solved according to a pre-constructed rotating speed and pitch angle collaborative distribution model, with the minimum power consumption of the power motor as the target; the target rotating speed and the target pitch angle are converted into motor PWM signals and rudder control signals respectively, and are output to the power motor and the rudder of the coaxial unmanned aerial vehicle. The above scheme can improve the control precision of the coaxial unmanned aerial vehicle, improve the response speed, enhance the adaptability to complex working conditions, optimize the energy efficiency and improve the safety.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, specifically to a coaxial UAV variable speed and variable pitch control method, device, UAV, and storage medium. Background Technology

[0002] Coaxial UAVs, with their unique aerodynamic layout, are widely used in numerous fields. They eliminate the tail rotor of traditional helicopters, employing two coaxially arranged propellers of the same diameter, controlling the aircraft's pitch and roll degrees of freedom via swashplates or vector steering mechanisms. Compared to other types of UAVs, coaxial UAVs offer advantages such as compact structure, high stability, and superior handling and hovering efficiency, playing a crucial role in military scenarios such as individual soldier reconnaissance and aerial surveillance.

[0003] However, current coaxial UAVs face numerous challenges in variable speed and pitch control. Existing control algorithms struggle to precisely coordinate the rotational speeds and pitch changes of the upper and lower rotors, resulting in poor flight attitude stability, particularly during acceleration, deceleration, turning, and navigating complex airflow environments. For instance, during high-speed braking, the lower rotor is prone to flapping due to lift bias; during horizontal movement, the gyroscopic torque generates significant reaction forces, degrading the UAV's maneuverability and flight stability. Furthermore, existing single-layer variable-pitch coaxial UAVs exhibit low efficiency in coordinating the variable-pitch and constant-pitch rotor layers, failing to fully leverage the UAV's performance advantages. These issues limit the application expansion of coaxial UAVs in more complex mission scenarios. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a coaxial unmanned aerial vehicle (UAV) variable speed and variable pitch control method, apparatus, UAV, and storage medium that overcome or at least partially solve the above problems.

[0005] According to one aspect of the present invention, a method for variable speed and pitch control of a coaxial unmanned aerial vehicle is provided, the method comprising: Acquire current flight status data of the drone and target flight commands; Determine the deviation between the target flight command and flight status data, and calculate the total lift requirement and attitude control torque requirement of the UAV based on the deviation; Based on the total lift requirement and attitude control torque requirement, and according to the pre-built speed and pitch coordination distribution model, with the goal of minimizing the power consumption of the motor, the target speed and target pitch angle of the upper and lower rotors are solved. The target rotational speed and target propeller pitch angle are converted into motor PWM signals and servo control signals, respectively, and output to the coaxial UAV's power motor and servo.

[0006] In some implementations, the pre-built rotational speed and pitch cooperative allocation model includes: Based on the equations for total lift demand and attitude control torque demand, and with the safe range of rotational speed and pitch as constraints and the goal of minimizing the power consumption of the motor, a rotational speed and pitch collaborative allocation model based on particle swarm optimization algorithm is constructed to solve for the target rotational speed and target pitch angle of the upper and lower rotors.

[0007] In some implementations, the pre-built rotational speed and pitch cooperative allocation model includes: Based on the flight process of coaxial UAVs, the flight of coaxial UAVs is divided into at least one of the following stages: take-off stage, cruise stage, hovering stage, mission execution stage, or landing. Based on the flight characteristics of each stage, the rules and priorities for speed control and pitch control of each flight stage are determined respectively, so that the coaxial UAV can find the determined control rules and priorities according to the flight commands or flight characteristics of the coaxial UAV during the mission, and then execute according to the control method and priority.

[0008] In some implementations, the control rules and priorities include: pitch priority + speed assist mode; fixed-pitch variable speed mode; anti-interference mode; speed coarse adjustment + pitch fine adjustment mode; speed dominant + pitch follower mode; low-speed smooth landing mode; PID adjustment mode.

[0009] In some embodiments, after determining the target rotational speed and target pitch angle of the upper and lower rotors, the method further includes: To address the coupling interference between the variable speed and variable pitch of the upper and lower rotors, at least one of the following coupling compensation terms is added: Rotation speed and pitch coupling compensation: when the rotational speed changes, the pitch angle is compensated according to the magnitude of the rotational speed deviation to avoid sudden changes in lift caused by rotational speed changes; Upper and lower rotor coupling compensation involves determining the actual values ​​of the difference in rotational speed and pitch between the upper and lower rotors, calculating the deviation between the actual values ​​and the target values, and inputting the deviations into the PID controller to correct the rotational speed and pitch commands of the upper and lower rotors, thereby eliminating coupling interference.

[0010] In some implementations, the total lift requirement and attitude control torque requirement of the UAV calculated based on the deviation specifically include: Total lift requirement: F_total = mg + k_z×Δz + k_vz×Δv_z, where m is the total mass of the UAV, g is the gravitational acceleration, Δz=z0-z is the position deviation, Δv_z is the vertical velocity deviation, and k_z and k_vz are the position scaling factor or velocity scaling factor, respectively. Attitude torque requirements: M_roll = k_φ×Δφ+ k_p×Δp; M_pitch = k_θ×Δθ + k_q×Δq; M_yaw = k_ψ×Δψ + k_r×Δr; where Δφ, Δθ, and Δψ are attitude deviations, Δp, Δq, and Δr are angular velocity deviations, k_φ, k_θ, and k are attitude scaling factors, and k_p, k_q, and k_r are angular velocity scaling factors.

[0011] In some implementations, the calculation formula for the control rule includes: Top rotor lift: F1 = 0.5 × ρ × S × C - L1 × n1 2 ×R 2 Lower rotor lift: F2 = 0.5×ρ×S×C_L2×n2 2 ×R 2 , where ρ is the air density, S is the rotor disk area, C_L1 and C_L2 are the lift coefficients of the upper and lower rotors respectively, where C_L = a×θ + b, a and b are aerodynamic parameters, and n1 and n2 are the rotational speeds of the upper and lower rotors respectively, R is the rotor radius; F1+F2 = F_total; Roll / pitch moment: M_roll / pitch = k_Δθ×Δθ, where Δθ=θ1-θ2 is the difference in pitch between the upper and lower rotor blades, and k_Δθ is the pitch difference moment coefficient; Yaw moment: M_yaw = k_Δn×Δn, where Δn=n1-n2 is the difference in rotational speed between the upper and lower rotors, and k_Δn is the torque coefficient for the difference in rotational speed; Optimization rules: Under the premise of satisfying the requirements of F_total and M_roll / pitch / yaw, with the goal of minimizing motor power consumption, the target rotational speeds n1 and n2 of the upper and lower rotors and the target pitch angles θ1 and θ2 are solved by the particle swarm optimization algorithm. The constraints are: n_min ≤ n1 / n2 ≤ n_max, θ_min ≤ θ1 / θ2 ≤ θ_max.

[0012] According to another aspect of the present invention, a coaxial unmanned aerial vehicle (UAV) variable speed and pitch control device is provided, the device comprising: The information acquisition module is suitable for acquiring the current flight status data of the UAV and target flight commands; The demand calculation module is adapted to determine the deviation between the target flight command and flight status data, and calculate the total lift demand and attitude control torque demand of the UAV based on the deviation. The objective solving module is suitable for solving the target rotational speed and target pitch angle of the upper and lower rotors based on the total lift requirement and attitude control torque requirement, according to the pre-built rotational speed and pitch coordination allocation model, with the goal of minimizing the power consumption of the power motor. The conversion output module is adapted to convert the target rotational speed and target pitch angle into motor PWM signals and servo control signals, respectively, and output them to the power motor and servo of the coaxial UAV.

[0013] According to another aspect of the present invention, a coaxial unmanned aerial vehicle (UAV) is provided, comprising: a processor and a memory arranged to store computer-executable instructions, wherein the executable instructions, when executed, cause the processor to perform the coaxial UAV variable speed and variable pitch control method according to any one of the above embodiments.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores one or more programs, which, when executed by a processor, implement the coaxial unmanned aerial vehicle variable speed and pitch control method according to any one of the above.

[0015] As can be seen from the above, the coaxial UAV variable speed and pitch control method disclosed in this invention constructs a closed-loop control framework of "state perception - demand analysis - collaborative adjustment - output execution" to improve the attitude stability and control performance of the coaxial UAV during flight, achieve more precise and efficient variable speed and pitch control, reduce the risk of propeller malfunction, improve the flight safety and reliability of the UAV under various complex working conditions, and expand its application scope.

[0016] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a coaxial unmanned aerial vehicle (UAV) variable speed and pitch control method according to an embodiment of the present invention is shown. Figure 2 A schematic diagram of a coaxial unmanned aerial vehicle according to an embodiment of the present invention is shown; Figure 3 A schematic diagram of a coaxial unmanned aerial vehicle (UAV) variable speed and pitch control device according to an embodiment of the present invention is shown. Figure 4 A schematic diagram of a coaxial unmanned aerial vehicle (flight control system) according to an embodiment of the present invention is shown. Detailed Implementation

[0018] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0019] Figure 1 A flowchart illustrating a coaxial UAV variable speed and pitch control method according to an embodiment of the present invention is shown. This method can be used for [see...] Figure 2 The method, as shown in the coaxial UAV structure, includes the following steps: Step S110: Acquire the current flight status data of the UAV and the target flight command; wherein, collect data from the status perception module: the current attitude angles of the UAV (roll φ, pitch θ, yaw ψ), angular velocity (p, q, r), position (x, y, z), rotor speeds n1 / n2, propeller pitch angles θ1 / θ2, and motor load torque T; parse the flight command: determine the target flight status (such as the target position (x0, y0, z0) when hovering, the target speed (v0) when cruising, and the target attitude angles (φ0, θ0, ψ0)) based on the input from the remote controller or mission system. Step S120: Determine the deviation between the target flight command and the flight status data, and calculate the total lift requirement and attitude control torque requirement of the UAV based on the deviation; Step S130: Based on the total lift requirement and attitude control torque requirement, according to the pre-built speed and pitch coordination allocation model, with the goal of minimizing the power consumption of the power motor, solve for the target speed and target pitch angle of the upper and lower rotors. In step S140, the target rotational speed and target propeller pitch angle are converted into motor PWM signals and servo control signals, respectively, and output to the coaxial UAV's power motor and servo.

[0020] Specifically, in combination Figure 2The coaxial UAV structure shown includes an irregularly shaped battery compartment 1, with two sets of coaxial propeller assemblies symmetrically arranged on its top. Each propeller assembly contains a propeller 2 rigidly fixed by a propeller clamp 3. The two are hinged to a propeller shaft 5 via a pin 4, forming a blade folding mechanism that can rotate around the pin. The upper and lower drive motors 6 are isomorphically designed, achieving axial folding through the rotatable folding mechanism. This allows the entire propulsion system to fold into a continuous curved surface with the outer contour of the irregularly shaped battery compartment, resulting in a compact, approximately cylindrical configuration. In operation, the upper and lower motors 6 drive their corresponding propellers 2 to rotate at high speed, forming a coaxial counter-rotating structure. The upper and lower propellers rotate in opposite directions, offsetting the counter-torque through aerodynamic coupling, achieving stable hovering and flight control. The four propellers (two on each side) work synchronously, generating upward lift through the aerodynamic shape of the blades to support the UAV's aerial attitude. The coaxial layout effectively shortens the fuselage length and improves structural compactness. The propellers 2 are connected to the propeller shaft 5 via a pin 4 through a propeller clamp 3, forming a rotatable joint. The motor assembly 6 adopts an axial hinge design, allowing it to rotate downwards around a pivot. Furthermore, it is also equipped with: State awareness module: including IMU (inertial measurement unit) (collects UAV attitude angle and angular velocity), GPS / BeiDou positioning module (collects position and velocity), rotor speed sensor (detects the upper and lower rotor speeds n1 and n2 in real time), pitch sensor (detects the upper and lower rotor pitch angles θ1 and θ2 in real time), and current sensor (collects motor current and calculates load torque T); Control module: A flight control chip based on the ARM Cortex-M7 core, running the variable speed and pitch control algorithm of this invention; The execution module includes brushless motors for the upper and lower rotors (to achieve speed regulation) and an electric pitch control mechanism (to adjust the pitch angle via servo motors). Step S120 involves calculating the target control variables, including: based on the deviation between the target flight state and the current state, calculating the total lift requirement F_total and the attitude control torque requirements (roll torque M_roll, pitch torque M_pitch, and yaw torque M_yaw). Total lift requirement: F_total = mg + k_z×Δz + k_vz×Δv_z, where m is the total mass of the UAV, g is the gravitational acceleration, Δz=z0-z is the position deviation, Δv_z is the vertical velocity deviation, and k_z and k_vz are the position / velocity ratio coefficients; Attitude torque requirements: M_roll = k_φ×Δφ + k_p×Δp, M_pitch = k_θ×Δθ + k_q×Δq, M_yaw = k_ψ×Δψ + k_r×Δr, where Δφ=φ0-φ is the attitude deviation, Δp is the angular velocity deviation, and k_φ, k_p, etc. are attitude / angular velocity ratio coefficients. In summary, this embodiment constructs a closed-loop control framework of "state perception - demand analysis - collaborative adjustment - output execution" to improve the attitude stability and control performance of coaxial UAVs during flight, achieve more precise and efficient variable speed and pitch control, reduce the risk of propeller malfunction, improve the flight safety and reliability of UAVs under various complex working conditions, and expand their application scope.

[0021] In some embodiments, the pre-built rotational speed and pitch cooperative allocation model in step S120 includes: Based on the equations for total lift demand and attitude control torque demand, and with the safe range of rotational speed and pitch as constraints and the goal of minimizing the power consumption of the motor, a rotational speed and pitch collaborative allocation model based on particle swarm optimization algorithm is constructed to solve for the target rotational speed and target pitch angle of the upper and lower rotors.

[0022] In some embodiments, the pre-built rotational speed and pitch cooperative allocation model includes: Based on the flight process of coaxial UAVs, the flight of coaxial UAVs is divided into at least one of the following stages: take-off stage, cruise stage, hovering stage, mission execution stage, or landing. Based on the flight characteristics of each stage, the rules and priorities for speed control and pitch control in each flight stage are determined respectively. This allows the coaxial UAV to find the determined control rules and priorities according to the flight commands or flight characteristics of the coaxial UAV during mission execution, and then execute according to the control method and priorities, and achieve optimization based on the particle swarm optimization algorithm.

[0023] In some embodiments, the control rules and priorities include: pitch priority + speed assist mode, which prioritizes pitch adjustment and then adjusts the speed according to the pitch value; fixed-pitch variable speed mode; anti-interference mode, which enters the anti-interference mode when affected by wind or other factors; speed coarse adjustment + pitch fine adjustment mode; speed-dominant + pitch-following mode; low-speed smooth landing mode; and PID adjustment mode. For details on the adjustment of the above modes, please refer to the specific embodiments.

[0024] In some embodiments, after determining the target rotational speed and target pitch angle of the upper and lower rotors, the method further includes: To address the coupling interference between the variable speed and variable pitch of the upper and lower rotors, at least one of the following coupling compensation terms is added: Rotation speed and pitch coupling compensation: when the rotational speed changes, the pitch angle is compensated according to the magnitude of the rotational speed deviation to avoid sudden changes in lift caused by rotational speed changes; Upper and lower rotor coupling compensation involves determining the actual values ​​of the difference in rotational speed and pitch between the upper and lower rotors, calculating the deviation between the actual values ​​and the target values, and inputting the deviations into the PID controller to correct the rotational speed and pitch commands of the upper and lower rotors, thereby eliminating coupling interference.

[0025] Specifically, speed-pitch coupling compensation: when the speed n changes, the pitch angle θ needs to be compensated by Δθ_comp = k_n × Δn (Δn = n*-n is the speed deviation, and k_n is the compensation coefficient), to avoid sudden changes in lift caused by speed changes; Upper and lower rotor coupling compensation: Collect the actual values ​​of the difference in rotational speed Δn between the upper and lower rotors and the difference in pitch Δθ. The deviation between these values ​​and the target values ​​Δn* / Δθ* is used as the compensation amount and input to the PID controller to correct the rotational speed and pitch commands of the lower rotor and eliminate coupling interference. In some embodiments, the calculation of the total lift requirement and attitude control torque requirement of the UAV based on the deviation specifically includes: Total lift requirement: F_total = mg + k_z×Δz + k_vz×Δv_z, where m is the total mass of the UAV, g is the gravitational acceleration, Δz=z0-z is the position deviation, Δv_z is the vertical velocity deviation, and k_z and k_vz are the position scaling factor or velocity scaling factor, respectively. Attitude torque requirements: M_roll = k_φ×Δφ+ k_p×Δp; M_pitch = k_θ×Δθ + k_q×Δq; M_yaw = k_ψ×Δψ + k_r×Δr; where Δφ, Δθ, and Δψ are attitude deviations, Δp, Δq, and Δr are angular velocity deviations, k_φ, k_θ, and k are attitude scaling factors, and k_p, k_q, and k_r are angular velocity scaling factors.

[0026] In some embodiments, the calculation formula for the control rule includes: Top rotor lift: F1 = 0.5 × ρ × S × C - L1 × n1 2 ×R 2 Lower rotor lift: F2 = 0.5×ρ×S×C_L2×n2 2 ×R 2 , where ρ is the air density, S is the rotor disk area, C_L1 and C_L2 are the lift coefficients of the upper and lower rotors respectively, where C_L = a×θ + b, a and b are aerodynamic parameters, and n1 and n2 are the rotational speeds of the upper and lower rotors respectively, R is the rotor radius; F1+F2 = F_total; Roll / pitch moment: M_roll / pitch = k_Δθ×Δθ, where Δθ=θ1-θ2 is the difference in pitch between the upper and lower rotor blades, and k_Δθ is the pitch difference moment coefficient; Yaw moment: M_yaw = k_Δn×Δn, where Δn=n1-n2 is the difference in rotational speed between the upper and lower rotors, and k_Δn is the torque coefficient for the difference in rotational speed; Optimization rules: Under the premise of satisfying the requirements of F_total and M_roll / pitch / yaw, with the goal of minimizing motor power consumption, the target rotational speeds n1 and n2 of the upper and lower rotors and the target pitch angles θ1 and θ2 are solved by the particle swarm optimization algorithm. The constraints are: n_min ≤ n1 / n2 ≤ n_max, θ_min ≤ θ1 / θ2 ≤ θ_max.

[0027] Compared with existing technologies, the variable speed and variable pitch control algorithm provided by this invention has the following advantages: 1. Improved control accuracy and response speed: Through variable speed and pitch coordinated distribution and coupling compensation, hovering position error is reduced and attitude recovery time is shortened to meet the requirements of high-precision operation; 2. Enhanced adaptability to complex operating conditions: The dynamically optimized "speed-pitch" distribution model can adjust parameters according to load torque T and airflow interference (identified by IMU angular velocity fluctuations), and can still maintain stable flight under 5kg load changes or level 6 wind interference; 3. Energy efficiency optimization: Through coordinated control of speed and pitch, motor power consumption is reduced by 12%-15%, and the driving range is increased by ≥12%; 4. Enhanced safety: By setting safe limits for speed and pitch, and combining load torque monitoring, the speed is automatically reduced and the pitch is adjusted when an overload occurs (T > T_max), thus preventing motor stall or blade damage.

[0028] ‌ Specific Implementation Examples Taking a 5kg-class coaxial dual-rotor UAV as an example, the hardware and algorithm parameters are set as follows: Drone specifications: Overall weight 5kg, maximum lift 12kg, upper and lower rotor diameter 1.2m; Motor parameters: Brushless DC motor, rated voltage 24V, rated speed 3000rpm, speed adjustment range 1200rpm-2800rpm; Pitch adjustment parameters: Pitch adjustment range 8°-20°, servo response time 40ms; Sensor parameters: IMU sampling frequency 100Hz, GPS positioning accuracy ±0.1m, speed sensor accuracy ±1rpm; Algorithm parameters: PID initial parameters (Kp=2.5, Ki=0.8, Kd=0.3), fuzzy control rule table (input variables are deviation Δθ, deviation change rate Δθ', output variables are ΔKp, ΔKi, ΔKd), lift calculation formula is L=0.5ρSv2CL (where ρ is air density, taken as 1.225kg / m³). 3S is the rotor area, taken as 1.131m². 2 v is the rotor linear velocity, v=πDn / 60, D is the rotor diameter; CL is the lift coefficient, which is positively correlated with the pitch angle β, and the fitting formula is CL=0.02β+0.1. The following explanation uses a single flight as an example. The flight process is divided into four stages, and the control algorithm, based on the particle swarm optimization principle, selects and executes different rules according to the characteristics of each stage: 1. Takeoff Phase Control The ground station issues the command "Take off to 10m altitude". The command parsing module sets the target altitude H=10m, attitude θ=φ=ψ=0°, and calculates the target lift Ltarget=mg+Fup using the lift formula (m is the total weight of the aircraft 5kg, g is taken as 9.8m / s²). 2 Fup is the lift force for takeoff acceleration, taken as 5N), so Ltarget = 5 × 9.8 + 5 = 54N; The data acquisition module collects the initial state (H=0m, n1=n2=1500rpm, β1=β2=8°) in real time and substitutes it into the lift formula to calculate the initial lift: v=π×1.2×1500 / 60≈94.25m / s, C L =0.02×8+0.1=0.26, Linitial = 0.5 × 1.225 × 1.131 × 94.252 × 0.26 ≈ 19.8 N, and the lift needs to be increased to 54 N through variable speed and pitch coordination; The collaborative control module determines that the takeoff phase has begun (H < 10m) and activates the "pitch priority + speed assist" algorithm. Pitch adjustment algorithm: Based on the lift difference ΔL = Ltarget − Linitial = 34.2N, combined with C L To calculate the required pitch angle Δβ based on the fitting relationship with β: First, fix the rotational speed n = 1500 rpm, keep v constant, and use L = 0.5ρSv 2 Substituting (0.02β+0.1) into L=54N, we get β≈15.2°. Therefore, we set β1 and β2 to increase from 8° to 15° at a rate of 0.5° / s (close to the calculated value, with a margin for speed adjustment). Speed-assisted adjustment algorithm: When the pitch angle rises to 12°, lift is calculated in real time. L = 0.5 × 1.225 × 1.131 × 94.252 × (0.02 × 12 + 0.1) ≈ 41.5 N, which is still lower than the target value, triggering speed adjustment. The speed adjustment amount is calculated by the PID controller. Δn = Kp × ΔL + Ki × ∫ΔL dt + Kd × d(ΔL) / dt. Substituting ΔL = 12.5 N, we get Δn ≈ 500 rpm. Therefore, we set n1 and n2 to increase from 1500 rpm to 2000 rpm at a rate of 100 rpm / s. The execution module drives the variable-pitch servo (adjusting the servo angle by 1.2° for every 0.5°, linearly adjusting the PWM duty cycle from 5% to 7.8%) and the motor (increasing the speed from 1500rpm to 2000rpm corresponding to an increase in the PWM duty cycle from 30% to 45%), providing real-time feedback on speed and pitch data: when H=5m, L=54N is detected, the speed increase is paused, and only the pitch increase is maintained; when H=10m, β=15°, n=2000rpm, then... L=0.5×1.225×1.131×(π×1.2×2000 / 60) 2 ×(0.02×15 + 0.1)≈62N Trigger PID fuzzy tuning (because ΔL=8N>5N, increase Kp to 3.0 to speed up the drop response), reduce n to 1900rpm, stabilize L at 50N (lift required for hovering), and switch to cruise phase control. 2. Cruise Phase Control The ground station issues a command to "cruise at a speed of 3 m / s". The command parsing module uses the target speed v = 3 m / s and heading angle ψ = 0° to calculate the required pitch angle for cruising using a dynamic model. θ=arcsin (v 2 / (gH))≈arcsin (9 / (9.8×10))≈5.2°, The target lift Lcruise = mg / cosθ≈5×9.8 / cos5.2°≈49.2N; The collaborative control module determines that the cruise phase has begun (H=10m≥10m, v=0→3m / s) and initiates the "constant-distance speed change" algorithm. Fixed pitch algorithm: Call the optimal efficiency pitch database calibrated by wind tunnel test (when v=3m / s, n=1800-2200rpm, β=12° corresponds to the lowest motor energy consumption), output fixed command β1=β2=12°, and lock the variable pitch servo PWM duty cycle to 6.6% (corresponding to 12° pitch). Dynamic speed adjustment algorithm: Initial speed n = 1900 rpm, calculate the current lift. L = 0.5 × 1.225 × 1.131 × (π × 1.2 × 1900 / 60)² × (0.02 × 1² + 0.1) ≈ 47.8 N, which is 1.4 N lower than the target value, and the speed v = 0. The speed adjustment is calculated using PID-fuzzy composite control. Position deviation Δv = 3 m / s, attitude deviation Δθ = 5.2°, input to PID controller to get basic Δn1 = Kp × Δv + Ki × Δθ = 2.5 × 3 + 0.8 × 5.2 ≈ 11.66 rpm; Fuzzy control determination (Δv=3m / s>2m / s, deviation change rate d(Δv) / dt=0.5m / s) 2 >0.3m / s 2 The output is ΔKp=+0.3. After adjustment, Kp=2.8. Finally, Δn=2.8×3 + 0.8×5.2≈13rpm. Therefore, n1 and n2 increase from 1900rpm to 2013rpm. Real-time monitoring of speed and attitude: When the actual speed v = 2.8 m / s, Δv = 0.2 m / s, Δθ = 4.8°, PID calculation. Δn = 2.8 × 0.2 + 0.8 × (5.2 - 4.8) = 0.56 + 0.32 ≈ 0.88 rpm. Fuzzy control judgment (Δv < 0.5 m / s), reduce Kp to 2.2 to avoid overshoot, and finally increase n1 and n2 by 50 rpm each (cumulative n = 2063 rpm) to make v reach 3 m / s; If a crosswind of 3 m / s is encountered, and the IMU detects φ = 1.2° (tilt angle), the anti-interference module determines it as wind interference and activates the speed difference compensation algorithm. Torque balance calculation: The disturbance torque generated by the crosswind is Wind = 0.5ρSvwind²DC T (C) T (where is the torque coefficient, taken as 0.03), substituting into vwind=3m / s, we get... Twind≈0.5×1.225×1.131×9×1.2×0.03≈0.6N⋅m; Rotation speed difference compensation: Torque difference between upper and lower rotors ΔT=K T Δn(K) T (This is the torque coefficient, taken as 0.012 N × m / rpm). ΔT = Twind = 0.6 N⋅m, solving for T gives... Δn=50rpm Therefore, the outputs Δn1 = +30rpm and Δn2 = -30rpm (adjusted by +2% and -2% respectively through the motor PWM duty cycle). After adjustment, φ returns to 0°, maintaining stable cruise. 3. Hovering Phase Control The ground station issues the command "hover at (Lon=116.4°, Lat=39.9°, H=10m)", and the command parsing module outputs the target attitude θ=φ=ψ=0°, the target position deviation ΔLon=ΔLat=0m, and the target lift Lhover=mg=49N; The collaborative control module determines that the system is in the hovering phase (v=0<0.5m / s) and initiates the "coarse speed adjustment + fine pitch adjustment" algorithm: Coarse speed adjustment algorithm: Calculate the base speed using the lift formula. nbase=60v / (πD), where v=2L / (ρSC) L Substituting L=49N and β=12° (C_L=0.34), we get v≈√(2×49 / (1.225×1.131×0.34))≈√(98 / 0.47)≈14.5m / s (here v is the rotor's relative airflow velocity, which is equal to the rotor's linear velocity when hovering). n_base=60×14.5 / (π×1.2)≈231rpm. The rotor's linear velocity should be v=πDn / 60. The lift when hovering... L=mg=0.5ρS(πDn / 60)2C L Substituting the data, we get n=(2mg×602) / (0.5ρSπ2D2C L =(2×5×9.8×3600) / (0.5×1.225×1.131×9.87×1.44×0.34)≈(352800) / (1.03)≈585rpm Based on the actual motor parameters, n1=n2=1800rpm (matching the rotor speed through the gear reduction mechanism), so the coarse speed adjustment is locked in the range of 1800±100rpm, and the motor PWM duty cycle is locked at 42%. Pitch fine-tuning algorithm: When GPS detects a Lat deviation of +0.3m (northward deviation), i.e., position deviation ΔLat = 0.3m and attitude deviation Δφ = 0.8° (due to the northward deviation causing a slight northward tilt of the fuselage), the pitch adjustment is calculated using PID-fuzzy control. PID basic calculation: Δβ=Kp×ΔLat + Ki×Δφ=2.5×0.3 + 0.8×0.8≈1.49°; Fuzzy rule determination: ΔLat=0.3m>0.2m, Δφ=0.8°<1°, output ΔKi=+0.2, after adjustment Ki=1.0, recalculate Δβ=2.5×0.3 + 1.0×0.8≈1.55°. Considering the rolling torque generated by the difference in pitch between the upper and lower rotors, set Δβ1=+0.5° (increase in lower rotor pitch, increase lift) and Δβ2=-0.5° (decrease in upper rotor pitch, decrease lift). Total rolling torque. M = ΔL × D / 2 = (0.5ρSv² × 0.02 × Δβ_total) × D / 2, substituting Δβ_{total} = 1°, we get... M≈0.5×1.225×1.131×(π×1.2×1800 / 60) 2 ×0.02×1×0.6≈12.3N×m, which is sufficient to offset the 0.3m positional deviation; The execution module drives the variable pitch servo: the duty cycle of the downrotor servo PWM increases from 6.6% to 6.8% (corresponding to β increasing from 12° to 12.5°), and the duty cycle of the uprotor servo decreases from 6.6% to 6.4% (corresponding to β decreasing from 12° to 11.5°). Feedback data shows that after 50ms, ΔLat=0.1m, triggering fuzzy tuning (ΔLat<0.2m, Kp is reduced to 2.0), adjusting Δβ1=+0.2° and Δβ2=-0.2°, and finally the position deviation is restored to within ±0.2m. 4. Landing Phase Control The ground station issues a "land to the ground" command. The command parsing module reduces the target altitude H from 10m to 0m, sets the descent speed v_down = 0.5m / s, and the target lift... Lland = mg − Fdown (Fdown is the deceleration force during descent, taken as 3N), so Lland = 49 − 3 = 46N; The collaborative control module determines that the descent phase has begun (H=10m→5m) and activates the "rotation speed-driven + pitch-following" algorithm: Rotational speed reduction algorithm: Initial n=1800rpm, β=12°, calculate the current lift. L=0.5×1.225×1.131×(π×1.2×1800 / 60) 2 ×0.34≈52N, which is higher than the target value of 6N. The speed adjustment is calculated using PID control. Δn = Kp × (L - L_{land}) + Kd × d (L) / dt = 2.5 × 6 + 0.3 × 2 ≈ 15.6 rpm. Set n1 and n2 to decrease from 1800 rpm to 1200 rpm at a rate of 80 rpm / s (a decrease of 80 rpm every 1 second corresponds to a linear decrease in the PWM duty cycle from 42% to 28%). Pitch following adjustment algorithm: When n drops to 1600 rpm, calculate... L=0.5×1.225×1.131×(π×1.2×1600 / 60) 2 ×0.34≈40.2N, which is 5.8N lower than the target value, triggering a pitch-following adjustment. The required pitch angle increase is then calculated using the lift formula. β=((2L) / (0.5\rho S v^2)-0.1) / 0.02, substitute into L=46N, v=π×1.2×1600 / 60≈100.5m / s, so β≈13.5°. However, since the lift needs to be gradually reduced during the descent phase, the pitch is adjusted to decrease from 12° to 8° at a rate of 0.3° / s (lagging the speed adjustment to ensure that the lift decreases slowly). When the barometer detects H=0.5m, the "low-speed smooth landing" sub-algorithm is activated: Fine-tuning of engine speed: n is reduced from 1200rpm to 1000rpm, and the speed is reduced to 40rpm / s to avoid excessive deceleration and hard landing; Pitch locking: β maintained at 8° (minimum safe pitch), calculation L=0.5×1.225×1.131×(π×1.2×1000 / 60) 2 ×(0.02×8 + 0.1)≈20.5N, which is less than mg=49N, so the drone falls slowly. Ground contact detection: When the IMU detects a vertical acceleration a = -9.8 m / s² 2 (Approaching free fall, ground contact is determined), immediately output the command n=0rpm, β=8°, the motor PWM duty cycle drops to 0%, the variable pitch servo locks, and a smooth landing is completed.

[0029] Combination Figure 3 As shown, the present invention also protects a coaxial UAV variable speed and pitch control device 300, the coaxial UAV variable speed and pitch control device 300 comprising: Information acquisition module 310 is suitable for acquiring current flight status data of the UAV and target flight commands; The demand calculation module 320 is adapted to determine the deviation between the target flight command and flight status data, and calculate the total lift demand and attitude control torque demand of the UAV based on the deviation. The objective solving module 330 is suitable for solving the target rotational speed and target pitch angle of the upper and lower rotors based on the total lift demand and attitude control torque demand, according to the pre-built rotational speed and pitch coordination allocation model, with the goal of minimizing the power consumption of the power motor. The conversion output module 340 is adapted to convert the target rotational speed and target pitch angle into motor PWM signals and servo control signals, respectively, and output them to the power motor and servo of the coaxial UAV.

[0030] According to this embodiment, constructing a closed-loop control framework of "state perception - demand analysis - collaborative adjustment - output execution" can improve the control accuracy of coaxial UAVs, increase response speed, enhance adaptability to complex working conditions, optimize energy efficiency, and improve safety.

[0031] In some embodiments, the pre-built rotational speed and pitch cooperative allocation model in the objective solution module 330 includes: Based on the equations for total lift demand and attitude control torque demand, and with the safe range of rotational speed and pitch as constraints and the goal of minimizing the power consumption of the motor, a rotational speed and pitch collaborative allocation model based on particle swarm optimization algorithm is constructed to solve for the target rotational speed and target pitch angle of the upper and lower rotors.

[0032] In some embodiments, the pre-built rotational speed and pitch cooperative allocation model in the objective solution module 330 further includes: Based on the flight process of coaxial UAVs, the flight of coaxial UAVs is divided into at least one of the following stages: take-off stage, cruise stage, hovering stage, mission execution stage, or landing. Based on the flight characteristics of each stage, the rules and priorities for speed control and pitch control of each flight stage are determined respectively, so that the coaxial UAV can find the determined control rules and priorities according to the flight commands or flight characteristics of the coaxial UAV during the mission, and then execute according to the control method and priority.

[0033] In some embodiments, the control rules and priorities include: pitch priority + speed assist mode; fixed pitch variable speed mode; anti-interference mode; speed coarse adjustment + pitch fine adjustment mode; speed dominant + pitch follower mode; low-speed smooth landing mode; PID adjustment mode.

[0034] In some embodiments, after solving for the target rotational speed and target pitch angle of the upper and lower rotors, the device further includes a coupling compensation module, adapted to: To address the coupling interference between the variable speed and variable pitch of the upper and lower rotors, at least one of the following coupling compensation terms is added: Rotation speed and pitch coupling compensation: when the rotational speed changes, the pitch angle is compensated according to the magnitude of the rotational speed deviation to avoid sudden changes in lift caused by rotational speed changes; Upper and lower rotor coupling compensation involves determining the actual values ​​of the difference in rotational speed and pitch between the upper and lower rotors, calculating the deviation between the actual values ​​and the target values, and inputting the deviations into the PID controller to correct the rotational speed and pitch commands of the upper and lower rotors, thereby eliminating coupling interference.

[0035] In some embodiments, the total lift requirement and attitude control torque requirement of the UAV calculated based on the deviation in the demand calculation module 320 specifically include: Total lift requirement: F_total = mg + k_z×Δz + k_vz×Δv_z, where m is the total mass of the UAV, g is the gravitational acceleration, Δz=z0-z is the position deviation, Δv_z is the vertical velocity deviation, and k_z and k_vz are the position scaling factor or velocity scaling factor, respectively. Attitude torque requirements: M_roll = k_φ×Δφ+ k_p×Δp; M_pitch = k_θ×Δθ + k_q×Δq; M_yaw = k_ψ×Δψ + k_r×Δr; where Δφ, Δθ, and Δψ are attitude deviations, Δp, Δq, and Δr are angular velocity deviations, k_φ, k_θ, and k are attitude scaling factors, and k_p, k_q, and k_r are angular velocity scaling factors.

[0036] In some embodiments, the calculation formula for the control rule includes: Top rotor lift: F1 = 0.5 × ρ × S × C - L1 × n1 2 ×R 2 Lower rotor lift: F2 = 0.5×ρ×S×C_L2×n2 2 ×R 2 , where ρ is the air density, S is the rotor disk area, C_L1 and C_L2 are the lift coefficients of the upper and lower rotors respectively, where C_L = a×θ + b, a and b are aerodynamic parameters, and n1 and n2 are the rotational speeds of the upper and lower rotors respectively, R is the rotor radius; F1+F2 = F_total; Roll / pitch moment: M_roll / pitch = k_Δθ×Δθ, where Δθ=θ1-θ2 is the difference in pitch between the upper and lower rotor blades, and k_Δθ is the pitch difference moment coefficient; Yaw moment: M_yaw = k_Δn×Δn, where Δn=n1-n2 is the difference in rotational speed between the upper and lower rotors, and k_Δn is the torque coefficient for the difference in rotational speed; Optimization rules: Under the premise of satisfying the requirements of F_total and M_roll / pitch / yaw, with the goal of minimizing motor power consumption, the target rotational speeds n1 and n2 of the upper and lower rotors and the target pitch angles θ1 and θ2 are solved by the particle swarm optimization algorithm. The constraints are: n_min ≤ n1 / n2 ≤ n_max, θ_min ≤ θ1 / θ2 ≤ θ_max.

[0037] It should be noted that the specific implementation methods of the above-mentioned device embodiments can be referred to the specific implementation methods of the corresponding methods described above, and will not be repeated here.

[0038] It should be noted that: The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. Various general-purpose devices can also be used in conjunction with the teachings herein. The required structure for constructing such devices is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0039] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0040] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0041] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0042] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0043] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the coaxial UAV variable speed and pitch control device according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can take the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0044] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the coaxial UAV variable speed and pitch control method described in any of the above method embodiments.

[0045] Figure 4 The diagram shows a structural schematic of an embodiment of the UAV of the present invention. The specific embodiments of the present invention do not limit the specific structure of the UAV.

[0046] like Figure 4As shown, the drone (including the flight control system) may include: a processor 402, a communications interface 404, a memory 406, and a communications bus 408.

[0047] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408. Communication interface 404 is used to communicate with other network elements such as clients or other servers. Processor 402 executes program 410, specifically performing the relevant steps in the above-described embodiment of the coaxial UAV variable speed and pitch control method for UAVs.

[0048] Specifically, program 410 may include program code that includes computer operation instructions.

[0049] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the drone may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0050] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0051] Specifically, program 410 can be used to enable processor 402 to perform the operations corresponding to the above-described embodiment of the coaxial UAV variable speed and pitch control method.

[0052] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. A method for variable speed and pitch control of a coaxial unmanned aerial vehicle (UAV), characterized in that, The method includes: Acquire current flight status data of the drone and target flight commands; Determine the deviation between the target flight command and flight status data, and calculate the total lift requirement and attitude control torque requirement of the UAV based on the deviation; Based on the total lift requirement and attitude control torque requirement, and according to the pre-built speed and pitch coordination distribution model, with the goal of minimizing the power consumption of the motor, the target speed and target pitch angle of the upper and lower rotors are solved. The target rotational speed and target propeller pitch angle are converted into motor PWM signals and servo control signals, respectively, and output to the coaxial UAV's power motor and servo. The pre-built rotational speed and pitch cooperative allocation model includes: Based on the flight process of coaxial UAVs, the flight of coaxial UAVs is divided into at least one of the following stages: take-off stage, cruise stage, hovering stage, mission execution stage, or landing. Based on the flight characteristics of each stage, the rules and priorities for speed control and pitch control of each flight stage are determined respectively, so that the coaxial UAV can find the determined control rules and priorities according to the flight commands or flight characteristics of the coaxial UAV during the mission, and then execute according to the control method and priority. The control rules and priorities include: pitch priority + speed assist mode; fixed pitch variable speed mode; anti-interference mode; speed coarse adjustment + pitch fine adjustment mode; speed-dominant + pitch follower mode; low-speed smooth landing mode; and PID adjustment mode.

2. The method according to claim 1, characterized in that, The pre-built rotational speed and pitch cooperative allocation model includes: Based on the equations for total lift demand and attitude control torque demand, and with the safe range of rotational speed and pitch as constraints and the goal of minimizing the power consumption of the motor, a rotational speed and pitch collaborative allocation model based on particle swarm optimization algorithm is constructed to solve for the target rotational speed and target pitch angle of the upper and lower rotors.

3. The method according to claim 1 or 2, characterized in that, After determining the target rotational speed and target pitch angle of the upper and lower rotors, the method further includes: To address the coupling interference between the variable speed and variable pitch of the upper and lower rotors, at least one of the following coupling compensation terms is added: Rotation speed and pitch coupling compensation: when the rotational speed changes, the pitch angle is compensated according to the magnitude of the rotational speed deviation to avoid sudden changes in lift caused by rotational speed changes; Upper and lower rotor coupling compensation involves determining the actual values ​​of the difference in rotational speed and pitch between the upper and lower rotors, calculating the deviation between the actual values ​​and the target values, and inputting the deviations into the PID controller to correct the rotational speed and pitch commands of the upper and lower rotors, thereby eliminating coupling interference.

4. The method according to claim 1, characterized in that, The total lift and attitude control torque requirements of the UAV, calculated based on the bias, specifically include: Total lift requirement: F_total = mg + k_z×Δz + k_vz×Δv_z, where m is the total mass of the UAV, g is the gravitational acceleration, Δz=z0-z is the position deviation, Δv_z is the vertical velocity deviation, and k_z and k_vz are the position scaling factor or velocity scaling factor, respectively. Attitude torque requirements: Roll torque M_roll = k_φ×Δφ+ k_p×Δp; Pitch torque M_pitch = k_θ×Δθ + k_q×Δq; Yaw torque M_yaw = k_ψ×Δψ + k_r×Δr; where Δφ, Δθ, and Δψ are attitude deviations, Δp, Δq, and Δr are angular velocity deviations, k_φ, k_θ, and k are attitude scaling factors, and k_p, k_q, and k_r are angular velocity scaling factors.

5. The method according to claim 1, characterized in that, The calculation formula for the control rules includes: Lift of upper and lower rotors: F1 = 0.5 × ρ × S × C - L1 × n1 2 ×R 2 F2 = 0.5×ρ×S×C_L2×n2 2 ×R 2 Where ρ is the air density, S is the rotor disk area, C_L1 and C_L2 are the lift coefficients of the upper and lower rotors respectively, where C_L1 = a×θ1 + b, C_L2 = a×θ2 + b, a and b are aerodynamic parameters, n1 and n2 are the rotational speeds of the upper and lower rotors respectively, θ1 and θ2 are the pitch angles of the upper and lower rotors respectively, and R is the rotor radius; F1+F2 = F_total; Rolling moment M_roll = k_Δθ×Δθ, pitching moment M_pitch = k_Δθ×Δθ, Δθ=θ1-θ2 is the difference in pitch between the upper and lower rotors, and k_Δθ is the pitch difference moment coefficient; Yaw moment: Myaw = k_Δn×Δn, where Δn=n1-n2 is the difference in rotational speed between the upper and lower rotors, and k_Δn is the torque coefficient for the difference in rotational speed; Optimization rules: Under the premise of satisfying the requirements of total lift F_total, roll moment M_roll, pitch moment M_pitch, and yaw moment M_yaw, with the goal of minimizing motor power consumption, the target rotational speed and target pitch angle of the upper and lower rotors are solved by particle swarm optimization algorithm. The constraints are: n_min ≤ n1 ≤ n_max, n_min ≤ n2 ≤ n_max, θ_min ≤ θ1 ≤ θ_max, θ_min ≤ θ2 ≤ θ_max.

6. A coaxial unmanned aerial vehicle (UAV) speed and pitch control device, characterized in that, The device includes: The information acquisition module is suitable for acquiring the current flight status data of the UAV and target flight commands; The demand calculation module is adapted to determine the deviation between the target flight command and flight status data, and calculate the total lift demand and attitude control torque demand of the UAV based on the deviation. The objective solving module is suitable for solving the target rotational speed and target pitch angle of the upper and lower rotors based on the total lift requirement and attitude control torque requirement, according to the pre-built rotational speed and pitch coordination allocation model, with the goal of minimizing the power consumption of the power motor. The conversion output module is adapted to convert the target rotational speed and target propeller pitch angle into motor PWM signals and servo control signals, respectively, and output them to the power motor and servo of the coaxial UAV. The pre-built rotational speed and pitch cooperative allocation model includes: Based on the flight process of coaxial UAVs, the flight of coaxial UAVs is divided into at least one of the following stages: take-off stage, cruise stage, hovering stage, mission execution stage, or landing. Based on the flight characteristics of each stage, the rules and priorities for speed control and pitch control of each flight stage are determined respectively, so that the coaxial UAV can find the determined control rules and priorities according to the flight commands or flight characteristics of the coaxial UAV during the mission, and then execute according to the control method and priority. The control rules and priorities include: pitch priority + speed assist mode; fixed pitch variable speed mode; anti-interference mode; speed coarse adjustment + pitch fine adjustment mode; speed-dominant + pitch follower mode; low-speed smooth landing mode; and PID adjustment mode.

7. A coaxial unmanned aerial vehicle, characterized in that, It includes a processor and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the coaxial unmanned aerial vehicle variable speed and pitch control method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, which, when executed by a processor, implement the coaxial UAV variable speed and pitch control method according to any one of claims 1-5.

Images