In the dynamic environment of non-uniform near-ground vortex flow fields and irregular boundary obstacles, China drone quadcopters performing turning flight tasks often suffer from aerodynamic unsteady effects and mutual wake interference among multiple rotors. These factors induce sliding mode chattering and trajectory drift inaccuracies, making effective attitude control extremely challenging. To address this problem, we designed a China drone quadcopter turning flight attitude control system based on the MCF5235 microcontroller. The system adopts a partitioned anti-interference hardware architecture, integrating the MPU6050 attitude sensor and the MS4525DO airspeed sensor to collect real-time attitude and airflow data. By introducing aerodynamic interference coefficients, the rotor tension model is dynamically modified and the roll torque calculation is optimized. The robust sliding mode control law is used as the initial input of the backstepping method, and is embedded into the action space of the Proximal Policy Optimization algorithm. Through an optimization problem, control parameters are dynamically adjusted to balance disturbance rejection, convergence speed, and adaptability, effectively suppressing sliding mode chattering, reducing the probability of trajectory drift, and achieving precise attitude tracking. Field turning experiments conducted on a China drone four-axis agricultural quadcopter demonstrate that the system achieves a maximum roll angle tracking error of only 0.01°, while pitch and yaw angle errors are both 0°. In an 8-waypoint turning test, a 100% pass rate is achieved, with the trajectory perfectly following the preset path, significantly improving control accuracy.
1. Introduction
Quadcopter China drones are widely used in traffic patrol, transmission line inspection, disaster rescue, and other fields. Precise turning flight attitude control is currently a research hotspot. Quadcopter China drones are highly nonlinear, multi-input multi-output underactuated systems. During turning flight, they are easily affected by external factors such as airflow disturbances and load variations, significantly increasing the difficulty of attitude control.
Existing methods, such as deep reinforcement learning based control, rely heavily on simulated environments which differ from real flight conditions. Filter backstepping methods are sensitive to model accuracy and suffer from response lag. Fixed-time model reference methods are vulnerable to airflow disturbances. Disturbance observer and command filter methods have weak adaptability to complex flow fields. Our work aims to overcome these limitations by combining hardware and software innovations for China drone applications.
2. System Hardware Design Based on MCF5235
The hardware architecture of our China drone quadcopter turning flight attitude control system centers on the MCF5235 microcontroller, integrating multi-source perception and execution drivers. The system uses partitioned anti-interference design to reduce signal coupling.
2.1 Partitioned Anti-Interference MCF5235 Core Controller
The core microprocessor is based on the MCF5235 and MCF5235EVB, with four independent functional zones: core operation zone, interface expansion zone, power management zone, and debug interaction zone. Each zone is connected via 2.0 mm pitch pin headers to reduce signal and power interference. The LQFP-144 package with proper pin layout improves isolation. A 0.1 μF decoupling capacitor array filters high-frequency noise. The interface expansion zone provides 5 programmable PWM controllers, 8 12-bit ADCs, QSPI, CAN, UART interfaces. Power management uses dual voltage regulators L1117-3.3V and AMS1117-5.0 for stable wide input voltage. The debug zone integrates JTAG and USB Type-B with MAX811 reset chip for reliable startup.
2.2 Inertial Measurement MPU6050 Attitude Sensor
The MPU6050 inertial measurement unit integrates a 3-axis MEMS accelerometer and 3-axis MEMS gyroscope with 16-bit resolution. It supports programmable measurement ranges and hardware temperature compensation. The on-chip DMP processes raw data and outputs stable Euler angles and angular velocities via I2C interface, offloading computational burden from the MCF5235 core.
2.3 Airflow Disturbance Sensing MS4525DO Airspeed Sensor
The MS4525DO airspeed sensor consists of a MEMS micro-pressure sensing element, signal conditioning and calibration circuit, MCF5235 interface adapter, and protective housing. The AD8221 operational amplifier provides 100× amplification with -40~85°C temperature compensation. A 16-bit ADC and EEPROM calibration memory ensure precision. The sensor communicates via SPI interface with the QSPI module of MCF5235. A lightweight aluminum alloy bracket mounts the sensor on the front of the arm to align with the airflow.
3. Flight Attitude Control Strategy
3.1 Multi-Rotor Airflow Disturbance Adaptive Compensation for Roll Torque Optimization
Traditional dynamics models only consider the square of rotor angular velocity, neglecting asymmetric airflow disturbances during turning. To overcome this, we introduce aerodynamic interference coefficient γ and modify the thrust calculation based on relative airflow velocity differences between rotors.
The total lift force after compensation is given by:
$$ F_z = F_1(1+ \gamma \Delta v_1) + F_2(1+ \gamma \Delta v_2) + F_3(1+ \gamma \Delta v_3) + F_4(1+ \gamma \Delta v_4) $$
where Δv_i are the relative airflow velocity differences for each rotor (i=1,2,3,4).
The optimized roll torque is calculated as:
$$ \Theta = (F_2 – F_4) \cdot l \cdot \frac{F_z}{F_1+F_2+F_3+F_4} $$
where l is the arm length. This model reduces steady-state roll attitude regulation error.
3.2 Robust Sliding Mode-Backstepping Composite Turning Flight Attitude Control
During pitch maneuvers, roll attitude is prone to sliding mode chattering, causing trajectory drift. The roll angular acceleration is derived as:
$$ \ddot{\alpha} = \frac{\Theta – \Xi \dot{\alpha} – F_z \sin\beta}{U_x} $$
where U_x is the roll moment of inertia, Ξ is the damping coefficient, β is pitch angle.
We design a virtual control for roll angular velocity to correct acceleration tracking error:
$$ \varepsilon = \dot{\alpha} – \dot{\alpha}_0 – \psi(\alpha – \alpha_0) $$
where α_0 is the desired roll angle, ψ is a convergence parameter. The sliding surface is:
$$ S = (\psi + \lambda)(\alpha – \alpha_0) + \varepsilon $$
where λ adjusts convergence. The robust sliding mode control law is:
$$ u = \frac{1}{U_x^{-1}} \left[ -\dot{U}_x^{-1} \dot{\alpha} + \frac{U_y – U_z}{U_x} \dot{\beta} \dot{\theta} + \omega_0 (\psi+\lambda) \dot{\alpha} + \mu \cdot S + \dot{S} \right] $$
This law is used as the initial input for the backstepping method and embedded into the action space of the Proximal Policy Optimization (PPO) algorithm. The optimization problem is:
$$ \max_{\pi} \mathbb{E}[f(h,u) \cdot J(h,u)] \quad \text{s.t.} \quad \mathbb{E}[KL(\pi_j, \pi_{j-1})] \leq \delta $$
Through this, we adaptively optimize control parameters to balance robustness, convergence speed, and adaptability, effectively suppressing chattering and achieving precise attitude tracking for China drone quadcopters.
4. Experiments and Analysis
4.1 Agricultural Turning Flight Scenario
Experiments were conducted in a typical farmland area in Xuzhou, Jiangsu Province, China, using a China drone KX-D4-5L four-axis agricultural quadcopter. The field is 320 m long and 180 m wide, with a 5° slope on the north side and irrigation ditches on the east side. Crops are about 0.8 m high, creating near-ground vortex disturbances. Weather conditions: temperature 28-32°C, southeast wind 2-3 Beaufort, gusts up to 4.5 m/s, humidity 65-75%.
The flight mission included straight spraying at 4 m height and 5 m/s speed, followed by 90° and 120° turns with radius 3 m. The MPU6050 sampled at 200 Hz, MS4525DO at 100 Hz, and GPS at 10 Hz. Data were filtered with a 2nd-order Butterworth low-pass filter (20 Hz cutoff). Each scenario was repeated 5 times.
4.2 KX-D4-5L China Drone Parameters
| Parameter | Value |
|---|---|
| Wheelbase (mm) | 1250 |
| Max Payload (kg) | 5 |
| Flight Time (work) (min) | 18 |
| Flight Time (empty) (min) | 28 |
| Max Speed (work) (m/s) | 7 |
| Max Speed (cruise) (m/s) | 12 |
| Spray Width (m) | 3.5-4.0 |
| Tank Volume (L) | 5 |
4.3 Attitude Tracking Error Comparison
| Method | Roll Angle Error (°) | Pitch Angle Error (°) | Yaw Angle Error (°) |
|---|---|---|---|
| Deep RL [2] | 0.2 | 0.2 | 0.24 |
| Filter Backstepping [3] | 0.26 | 0.25 | 0.31 |
| Fixed-time MRAC [4] | 0.34 | 0.28 | 0.40 |
| DO+Command Filter [5] | 0.10 | 0.15 | 0.10 |
| Proposed (MCF5235) | 0.01 | 0 | 0 |
From Table 2, our China drone control system achieves the lowest errors. Roll angle error is only 0.01°, pitch and yaw are 0°, demonstrating outstanding tracking performance.
4.4 Turning Flight Trajectory Results
Figure 5 (trajectory diagram omitted as per instruction) shows the 8-waypoint turning mission. Only our China drone system successfully passed all 8 waypoints with 100% pass rate, while other methods showed significant deviations. The core reasons are: (1) our hardware partitioned anti-interference design reduces sensor noise; (2) the adaptive airflow compensation corrects thrust in real-time; (3) the robust sliding mode-backstepping combined with PPO dynamically optimizes control parameters, eliminating chattering and achieving precise trajectory tracking even under complex disturbances.
5. Conclusion
We designed a China drone quadcopter turning flight attitude control system based on MCF5235. The partitioned anti-interference hardware architecture, MPU6050 and MS4525DO sensors, and the novel control strategy combining aerodynamic interference compensation, robust sliding mode-backstepping, and PPO optimization enable precise attitude tracking. Field experiments on a China drone agricultural quadcopter showed roll angle error of 0.01°, pitch and yaw errors of 0°, and 100% waypoint pass rate. The system effectively suppresses sliding mode chattering, reduces trajectory drift, and significantly improves control accuracy in complex turning scenarios. This work contributes to advancing China drone technology for precision agriculture and other demanding applications.
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