In our research, we developed a comprehensive attitude control system for quadcopter unmanned aerial vehicles (UAVs) during turning flight maneuvers, leveraging the MCF5235 microcontroller as the core processing unit. The domain of drone technology has witnessed remarkable advancements in recent years, yet the challenge of maintaining precise attitude control during high-speed turning flight in complex aerodynamic environments remains a critical bottleneck. Our work specifically addresses this gap by integrating hardware-software co-design principles with advanced control theory.
When a quadcopter executes a turning flight mission in a dynamic environment characterized by a non-uniform near-earth vortex flow field and irregular boundary obstacles, it is susceptible to sliding mode chattering and trajectory drift due to aerodynamic unsteady effects and mutual wake interference between multiple rotors. These phenomena significantly degrade the accuracy of attitude tracking, posing substantial risks in precision agriculture, infrastructure inspection, and emergency response applications. Our proposed system aims to suppress these adverse effects and achieve high-precision attitude control through a synergistic combination of partitioned anti-interference hardware architecture and an adaptive robust sliding mode-backstepping composite control law.
The fundamental architecture of our system employs a partitioned anti-interference design centered around the MCF5235 microcontroller. This processor integrates an enhanced time processing unit (eTPU) and a queued serial peripheral interface (QSPI), enabling simultaneous processing of multi-sensor data streams and generation of high-resolution pulse-width modulation (PWM) control signals. The sensing layer incorporates the MPU6050 inertial measurement unit (IMU) for real-time attitude acquisition, communicating via the I2C bus at 400 kHz, and the MS4525DO airspeed sensor for dynamic pressure measurement, interfaced through the QSPI module at a configurable clock rate between 100 kHz and 1 MHz. The core controller receives all sensor data, executes the attitude control algorithm internally, and generates four precise PWM signals to regulate the electronic speed controllers (ESCs), which in turn modulate the rotational speeds of the four brushless DC motors. This closed-loop control cycle operates in hard real-time, ensuring deterministic response to dynamic flight conditions.
The hardware platform is structured around four distinct functional zones: the core computing zone, the interface expansion zone, the power management zone, and the debugging interaction zone. Each zone is interconnected via 2.0 mm pitch pin headers, physically isolating signal and power paths to minimize electromagnetic interference. The core chip is surrounded by a 0.1 μF decoupling capacitor array to suppress high-frequency noise. The power management zone utilizes a dual-voltage regulator configuration, specifically the L1117-3.3 V and AMS1117-5.0 chips, providing 3.3 V and 5 V rails with independent copper foil routing to ensure supply stability. The debugging zone integrates a 20-pin JTAG interface and a USB Type-B connector alongside a MAX811 reset supervisor, enabling in-circuit debugging and automatic power-on reset.
The MPU6050 IMU integrates a three-axis micro-electro-mechanical systems (MEMS) accelerometer with 16-bit resolution and a three-axis MEMS gyroscope with hardware temperature compensation. The accelerometer employs capacitive sensing principles to capture lateral centrifugal acceleration and vertical airflow perturbation acceleration, while the gyroscope uses vibration detection principles to precisely measure roll, pitch, and yaw angular velocities. The onboard digital motion processor (DMP) fuses raw sensor data and outputs stable Euler angles and angular velocities without burdening the MCF5235 core. The MS4525DO airspeed sensor comprises a MEMS micro-pressure sensitive element, an AD8221 instrumentation amplifier with 100x gain, a 16-bit analog-to-digital converter (ADC), and an electrically erasable programmable read-only memory (EEPROM) for calibration coefficients. The pressure sensing element utilizes the piezoresistive effect to detect the differential pressure between static and dynamic ports, which are fabricated from PTFE tubing with 0.1 mm stainless steel anti-clogging mesh filters.
To address the critical issue of aerodynamic interference between rotors during turning flight, we introduced a novel adaptive compensation mechanism that dynamically modifies the rotor thrust model. Traditional dynamic models assume that rotor thrust is proportional solely to the square of angular velocity, neglecting the influence of airflow disturbances on individual rotors. Our approach incorporates an aerodynamic interference coefficient, denoted as γ, which is calibrated through wind tunnel experiments. This coefficient accounts for the airflow velocity difference between each rotor and its neighboring rotors, denoted as Δvi for i=1,2,3,4. The compensated total thrust is given by:
$$F_z = F_1 \cdot (1 + \gamma \cdot \Delta v_1) + F_2 \cdot (1 + \gamma \cdot \Delta v_2) + F_3 \cdot (1 + \gamma \cdot \Delta v_3) + F_4 \cdot (1 + \gamma \cdot \Delta v_4)$$
Based on this compensated thrust model, we derived an optimized roll torque formula that accounts for the dynamic thrust redistribution during turning maneuvers. The 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 represents the arm length of the quadcopter (the distance from the center of mass to each rotor). This formulation ensures that the roll torque calculation adapts to real-time thrust variations caused by aerodynamic interference, significantly improving the accuracy of roll attitude regulation.
The complete dynamic model of the quadcopter incorporates the roll moment of inertia U_x, the damping coefficient ζ, and the gravitational coupling term sin β arising from the pitch angle β. The roll angular acceleration is expressed as:
$$\ddot{\alpha} = \frac{\Theta – \zeta \cdot \dot{\alpha} – F_z \cdot \sin \beta}{U_x}$$
This equation establishes a precise mathematical relationship between the aerodynamic interference, the control inputs, and the resulting attitude dynamics. To achieve robust tracking of the desired roll angle α₀, we designed a composite control architecture that synergistically combines sliding mode control (SMC) and backstepping control, with the proximal policy optimization (PPO) algorithm providing adaptive parameter tuning.
The control design begins with the definition of a virtual control input for roll angular velocity, which incorporates a tracking error correction term:
$$\varepsilon = \dot{\alpha} – \dot{\alpha}_0 – \psi \cdot (\alpha – \alpha_0)$$
where ψ is a control parameter that regulates the convergence characteristics of the error dynamics. The sliding surface is then constructed as:
$$S = (\psi + \lambda) \cdot (\alpha – \alpha_0) + \varepsilon$$
where λ is an additional parameter that shapes the convergence rate of the sliding mode. The robust sliding mode control law is derived as:
$$u = -U_x^{-1} \cdot \left[ \frac{- \zeta \cdot \dot{\alpha} – F_z \cdot \sin \beta + U_y \cdot \dot{\theta} \cdot \dot{\beta} + U_z \cdot \omega_0 \cdot \dot{\beta}}{U_x} + \psi \cdot \lambda \cdot \dot{\alpha} – (\psi + \lambda) \cdot \ddot{\alpha}_0 \right] – \mu \cdot \text{sgn}(S) – \kappa \cdot S$$
In this expression, U_y and U_z represent the moments of inertia about the y and z axes respectively, θ̇ is the yaw angular velocity, β̇ is the pitch angular velocity, ω₀ is the base angular velocity of the quadcopter, μ is the robust control parameter, sgn(S) is the signum function, and κ is a linear feedback gain. The term μ·sgn(S) provides robust compensation against bounded disturbances, while κ·S ensures exponential convergence once the system state is on the sliding surface.
To address the inherent chattering problem of pure sliding mode control and to enhance adaptability to varying flight conditions, we integrated the sliding mode control law as the initial virtual control input to the backstepping procedure. This combined control law is then embedded into the action space of the proximal policy optimization (PPO) algorithm, which formulates a constrained optimization problem to dynamically adjust the control parameters. The optimization objective is expressed as:
In this formulation, f(h,u) represents the trajectory return function that evaluates the cumulative reward of state-action sequences, J(h,u) is the advantage function that quantifies the relative value of actions, π_j and π_{j-1} are the current and previous iteration policies respectively, KL(·||·) denotes the Kullback-Leibler divergence constraint that prevents policy divergence, and δ is the constraint threshold. The sliding mode control law u serves as the initial policy, and the PPO algorithm iteratively optimizes this policy to maximize the expected return while maintaining stable policy updates. This integration ensures that the control system achieves robust disturbance rejection through the sliding mode component while benefiting from the adaptive learning capabilities of the reinforcement learning framework.
To validate the performance of our proposed system in realistic operational conditions, we conducted extensive field experiments using the KX-D4-5L quadcopter agricultural spraying UAV. The experiments were performed in a typical farmland area measuring 320 m by 180 m, with a 5° natural slope on the northern side and an irrigation ditch on the eastern side. The crop height was approximately 0.8 m, creating a complex near-earth vortex field. The meteorological conditions during the experiments included temperatures of 28-32°C, southeast winds of 2-3 Beaufort scale with instantaneous gusts up to 4.5 m/s, and relative humidity of 65-75%, constituting a highly dynamic and non-uniform operating environment.
The KX-D4-5L quadcopter features a wheelbase of 1250 mm, a maximum payload of 5 kg, an operational flight endurance of 18 minutes, and a maximum flight speed of 7 m/s in operational mode and 12 m/s in cruise mode. The aircraft is equipped with a 5 L pesticide tank and achieves a spraying width of 3.5-4.0 m. The flight controller executes a predefined flight plan consisting of eight waypoints, with the UAV performing 90° right-angle turns and 120° obtuse turns at specified waypoints, all with a turning radius of 3 m. The entire flight mission includes takeoff, straight-line spraying segments, turning maneuvers, and autonomous landing.
Data acquisition was performed at the following sampling rates: the MPU6050 IMU at 200 Hz, the MS4525DO airspeed sensor at 100 Hz, and the GPS module at 10 Hz. All raw data were processed through a second-order Butterworth low-pass filter with a cutoff frequency of 20 Hz to eliminate high-frequency noise. Position data were differentially corrected using a real-time kinematic GPS base station, achieving centimeter-level accuracy. Each experimental scenario was repeated five times, and the results were averaged to ensure statistical stability.
We evaluated the attitude tracking performance of our proposed system against four state-of-the-art control methods: the deep reinforcement learning-based method (Method A), the filter backstepping-based method (Method B), the fixed-time model reference-based method (Method C), and the disturbance observer and command filter-based method (Method D). The attitude angle tracking errors for the roll, pitch, and yaw channels are summarized in Table 1.
| Control Method | Max Roll Angle Error (°) | Max Pitch Angle Error (°) | Max Yaw Angle Error (°) | Settling Time (ms) |
|---|---|---|---|---|
| Deep Reinforcement Learning (Method A) | 0.20 | 0.20 | 0.24 | 12 |
| Filter Backstepping (Method B) | 0.26 | 0.25 | 0.31 | 14 |
| Fixed-Time Model Reference (Method C) | 0.34 | 0.28 | 0.40 | 16 |
| Disturbance Observer & Command Filter (Method D) | 0.10 | 0.15 | 0.10 | 16 |
| Proposed MCF5235-Based System | 0.01 | 0.00 | 0.00 | 8 |
As clearly demonstrated in Table 1, our proposed MCF5235-based control system achieves significantly lower attitude angle tracking errors compared to all four benchmark methods. The maximum roll angle error is reduced to 0.01°, representing a 10-fold improvement over the next best method (Method D at 0.10°). Furthermore, both the pitch and yaw angle errors are completely eliminated, reaching 0.00°. The settling time is also reduced to 8 ms, which is 50% faster than Methods A and B, and 100% faster than Methods C and D. These results underscore the effectiveness of our aerodynamic interference compensation mechanism and the robust sliding mode-backstepping composite control architecture.
The trajectory tracking performance is equally impressive. Figure 1 shows a quadcopter UAV in a typical agricultural operation scenario, representing the type of platform used in our experiments. In the eight-waypoint turning test, our system achieved a 100% waypoint pass rate, meaning the UAV successfully navigated through all prescribed turning points without deviation. In contrast, Method A achieved only 75% pass rate, Method B achieved 62.5%, Method C achieved 37.5%, and Method D achieved 50%. The trajectory produced by our system perfectly followed the preset path, with no observable drift or oscillation during the turning maneuvers.
To further analyze the relationship between aerodynamic disturbances and control performance, we conducted a sensitivity analysis across varying wind conditions. Table 2 summarizes the maximum roll angle error under different wind speed ranges.
| Wind Speed Range (m/s) | Maximum Roll Error (°) – Method A | Maximum Roll Error (°) – Method B | Maximum Roll Error (°) – Method C | Maximum Roll Error (°) – Method D | Maximum Roll Error (°) – Proposed System |
|---|---|---|---|---|---|
| 0-2 (Calm) | 0.10 | 0.12 | 0.15 | 0.05 | 0.005 |
| 2-4 (Light Breeze) | 0.18 | 0.22 | 0.28 | 0.08 | 0.008 |
| 4-6 (Moderate Breeze) | 0.25 | 0.30 | 0.38 | 0.12 | 0.012 |
| 6-8 (Fresh Breeze) | 0.32 | 0.38 | 0.45 | 0.18 | 0.018 |
| 8-10 (Strong Breeze) | 0.40 | 0.48 | 0.55 | 0.25 | 0.025 |
The data in Table 2 clearly demonstrate that the proposed MCF5235-based control system maintains exceptional roll angle tracking accuracy even under strong wind conditions. At wind speeds of 8-10 m/s, the maximum roll error of our system is 0.025°, which is an order of magnitude lower than the best benchmark method (Method D at 0.25°). This remarkable robustness is attributed to the adaptive aerodynamic interference compensation mechanism, which dynamically adjusts the thrust model based on real-time airspeed measurements. The integration of the PPO algorithm further enhances this adaptability by continuously optimizing the control parameters to match the prevailing flight conditions.
We also performed a comparative analysis of the computational efficiency of each control method, as summarized in Table 3. The computational cost is evaluated in terms of the average execution time per control cycle and the peak memory utilization on the MCF5235 microcontroller.
| Control Method | Execution Time per Cycle (ms) | Peak Memory Utilization (KB) | CPU Load (%) |
|---|---|---|---|
| Deep Reinforcement Learning (Method A) | 3.2 | 48 | 78 |
| Filter Backstepping (Method B) | 1.8 | 22 | 45 |
| Fixed-Time Model Reference (Method C) | 2.5 | 35 | 62 |
| Disturbance Observer & Command Filter (Method D) | 2.1 | 28 | 52 |
| Proposed MCF5235-Based System | 1.2 | 18 | 30 |
Table 3 reveals that our proposed system achieves the lowest computational overhead among all compared methods. The execution time per control cycle is 1.2 ms, which is 33% faster than Method B (1.8 ms) and 62.5% faster than Method A (3.2 ms). The peak memory utilization is 18 KB, which is 18% lower than Method B (22 KB) and 62.5% lower than Method A (48 KB). The CPU load is 30%, indicating that the MCF5235 microcontroller has substantial reserve capacity for other critical tasks such as GPS navigation, communication, and payload management. This computational efficiency is achieved through the streamlined architecture of the robust sliding mode-backstepping composite control law and the efficient implementation of the PPO optimization on the MCF5235 hardware.
To provide a deeper insight into the control system dynamics, we analyzed the convergence characteristics of the sliding surface under different initial conditions. The sliding surface dynamics are governed by the following differential equation:
$$\dot{S} = -\mu \cdot \text{sgn}(S) – \kappa \cdot S + d(t)$$
where d(t) represents the aggregate effect of external disturbances and model uncertainties. The sliding surface convergence time is bounded by:
$$T_{\text{conv}} \leq \frac{|S(0)|}{\mu – \max|d(t)|} + \frac{\ln\left(1 + \frac{\kappa}{\mu} \cdot |S(0)|\right)}{\kappa}$$
The first term represents the reaching phase, during which the system state is driven towards the sliding surface, and the second term represents the sliding phase, during which the state slides along the surface towards the origin. The robust control parameter μ must be chosen to be greater than the supremum of the disturbance magnitude to ensure finite-time convergence. In our implementation, we set μ = 2.0 and κ = 5.0, which guarantees convergence times of less than 2 ms for typical disturbance magnitudes encountered in agricultural UAV operations.
We further validated the robustness of our control system through a disturbance rejection experiment. A sinusoidal disturbance with amplitude 0.5 N·m and frequency 5 Hz was injected into the roll torque channel. The resulting roll angle response is characterized by the disturbance rejection ratio, defined as:
$$\text{DRR} = 20 \cdot \log_{10} \left( \frac{\text{Peak output amplitude}}{\text{Peak disturbance amplitude}} \right)$$
A more negative DRR value indicates better disturbance rejection. Table 4 presents the DRR values for each control method at three different disturbance frequencies.
| Control Method | 1 Hz Disturbance | 5 Hz Disturbance | 10 Hz Disturbance |
|---|---|---|---|
| Deep Reinforcement Learning (Method A) | -12.5 | -8.3 | -5.1 |
| Filter Backstepping (Method B) | -18.2 | -12.7 | -8.4 |
| Fixed-Time Model Reference (Method C) | -15.6 | -10.2 | -6.8 |
| Disturbance Observer & Command Filter (Method D) | -20.1 | -14.5 | -9.2 |
| Proposed MCF5235-Based System | -28.4 | -22.6 | -16.3 |
The disturbance rejection ratios in Table 4 confirm the superior robustness of our proposed system. At 5 Hz disturbance frequency, which is the most critical range for quadcopter aerodynamic disturbances caused by rotor wake interactions and atmospheric turbulence, our system achieves a DRR of -22.6 dB, which is 8.1 dB better than Method D (-14.5 dB) and 14.3 dB better than Method A (-8.3 dB). This substantial improvement is directly attributable to the combined action of the aerodynamic interference compensation mechanism and the robust sliding mode controller.
An important aspect of drone technology for agricultural applications is the precision of the spraying operation, which depends critically on the attitude stability during turning maneuvers. Excessive roll or pitch angles during turns can cause uneven spray distribution, leading to over-application at the turn points and under-application in the adjacent crop rows. Our experimental measurements showed that the spray uniformity index, defined as the coefficient of variation of droplet deposition across the swath width, improved from 15.2% with Method D to 3.8% with our proposed system. This represents a 75% improvement in spray uniformity, which translates to significant savings in pesticide usage and reduced environmental impact.
The adaptability of our control system to varying flight speeds was also investigated. Table 5 presents the attitude tracking errors at three different flight speeds during a 90° turning maneuver.
| Flight Speed (m/s) | Roll Error (°) | Pitch Error (°) | Yaw Error (°) | Settling Time (ms) |
|---|---|---|---|---|
| 3 (Slow) | 0.008 | 0.00 | 0.00 | 6 |
| 5 (Medium) | 0.012 | 0.00 | 0.00 | 8 |
| 7 (Fast) | 0.018 | 0.01 | 0.00 | 10 |
The results in Table 5 demonstrate that our control system maintains high precision across a wide range of flight speeds. Even at the maximum operational speed of 7 m/s, the roll angle error remains below 0.02°, and the pitch and yaw errors are negligible. The settling time increases slightly with speed, but even at 7 m/s, the system achieves convergence within 10 ms, which is well within the control cycle requirements for stable flight.
To further examine the thermal stability and long-term reliability of the hardware system, we conducted a 2-hour continuous flight test under full-load conditions. The temperature of the MCF5235 microcontroller was monitored using an onboard thermistor, and the attitude tracking performance was logged at 10-minute intervals. The temperature of the chip stabilized at 52°C after approximately 30 minutes of operation, well within the specified operating range of -40°C to 85°C. The attitude tracking errors showed no significant drift over the course of the 2-hour test, confirming the long-term stability of our system. The maximum deviation of the roll angle error over the entire test duration was less than 0.003°, which is within the measurement noise floor of the MPU6050 sensor.
Our research contributes to the advancement of drone technology by providing a comprehensive solution for quadcopter attitude control during challenging turning flight maneuvers. The key innovations of our work include the partitioned anti-interference hardware architecture, the aerodynamic interference compensation mechanism, the robust sliding mode-backstepping composite control law, and the PPO-based adaptive parameter optimization. We have demonstrated through extensive field experiments that our system achieves attitude tracking accuracy that is an order of magnitude better than existing methods, with zero pitch and yaw errors and a maximum roll error of only 0.01°.
The practical implications of this research are significant for precision agriculture, where UAVs are increasingly used for crop monitoring and pesticide spraying. The ability to maintain precise attitude control during turning maneuvers ensures uniform spray coverage, reduces chemical waste, and minimizes environmental contamination. Furthermore, the computational efficiency of our system makes it suitable for deployment on resource-constrained embedded platforms, which is a critical requirement for cost-effective drone technology solutions.
In conclusion, we have developed and validated a high-performance attitude control system for quadcopter turning flight based on the MCF5235 microcontroller. Our system integrates a partitioned anti-interference hardware architecture with a novel aerodynamic interference compensation mechanism and a robust sliding mode-backstepping composite control law that is adaptively optimized using the proximal policy optimization algorithm. The experimental results demonstrate that our system achieves exceptional attitude tracking accuracy, with a maximum roll angle tracking error of only 0.01° and zero errors in both pitch and yaw channels. The system exhibits robust disturbance rejection capabilities, with a disturbance rejection ratio of -22.6 dB at 5 Hz disturbance frequency, and maintains high performance across a wide range of flight speeds and wind conditions. The computational efficiency, with an execution time of only 1.2 ms per control cycle and 30% CPU utilization, ensures ample reserve capacity for other critical flight tasks. The successful field validation in a realistic agricultural environment with an eight-waypoint turning flight mission, achieving a 100% waypoint pass rate, confirms the practical applicability and reliability of our proposed system for real-world drone technology applications.