Design and Experimental Evaluation of a Modular and Extensible Flight Control Board for Unmanned Aerial Vehicles
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Keywords

Unmanned aerial vehicle
Flight control board
Real-time embedded systems
Sensor fusion
Robustness analysis

How to Cite

Design and Experimental Evaluation of a Modular and Extensible Flight Control Board for Unmanned Aerial Vehicles. (2026). ADBA Computer Science, 3(2), 111-116. https://doi.org/10.69882/adba.cs.2026076

Abstract

Commercial flight control units used in unmanned aerial vehicles are typically delivered as closed, black-box platforms, which restricts their utility for academic research and rapid prototyping. This study presents the design, fabrication, and multi-stage evaluation of a modular and extensible flight control board for a four-rotor unmanned aerial vehicle. The board is built around a high-performance microcontroller with a hardware floating-point unit and a real-time operating system that schedules sensor acquisition, signal processing, and data transmission as concurrent tasks. A six-degree-of-freedom dynamic model of the vehicle is derived and used to design a cascade proportional-integral-derivative controller, in which an outer loop regulates attitude and a faster inner loop regulates angular rate. Raw inertial measurements are conditioned through a hybrid signal-processing chain combining an 81 Hz infinite impulse response (IIR) Biquad low-pass filter with a recursive state estimator, suppressing vibration-induced noise while limiting phase lag. On the fabricated hardware, the implemented IIR Biquad filter reduced the root-mean-square noise of the gyroscope signal by 60.2\% (from 1.002$^\circ$/s to 0.399$^\circ$/s) while adding a 2.78 ms phase delay. In 6-DoF numerical simulations, the cascade controller achieved rise times below 0.77 s for the roll and pitch axes and eliminated steady-state error. Comprehensive disturbance analyses demonstrated that the controller maintains stable closed-loop behavior, restricting peak attitude deviation to under 1$^\circ$ during a 3.0 N$\cdot$m external wind disturbance torque. The telemetry subsystem sustained a stable 100 Hz communication link with a 0.18\% packet loss rate and an 18 ms latency, confirming its suitability as a transparent, modular, and reproducible platform for advanced avionics research.

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References

Abro, G. E. M., S. A. B. M. Zulkifli, and V. S. Asirvadam, 2021 Performance evaluation of Newton–Euler and quaternion mathematics-based dynamic models for an under-actuated quadrotor UAV. In 2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), pp. 142–147.

Boll, A., F. Brokhausen, T. Amorim, T. Kehrer, and A. Vogelsang, 2021 Characteristics, potentials and limitations of open-source Simulink projects for empirical research. Software and Systems Modeling 20: 2111–2130.

Dalimunthe, E. R., N. D. Ananda, J. P. Sembiring, M. A. S. Faidar, E. Pranita, et al., 2025 Implementation of Kalman filter on PID-based quadcopter for controlling pitch angle. AVITEC 7: 41–52.

Duffy, D. G., 2016 Advanced Engineering Mathematics with MATLAB. CRC Press, fourth edition.

Euston, M., P. Coote, R. Mahony, J. Kim, and T. Hamel, 2008 A complementary filter for attitude estimation of a fixed-wing UAV. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 340–345.

Hua, M.-D., T. Hamel, P. Morin, and C. Samson, 2013 Introduction to feedback control of underactuated VTOL vehicles: A review of basic control design ideas and principles. IEEE Control Systems Magazine 33: 61–75.

Kalman, R. E., 1960 A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82: 35–45.

Li, Y., J. Na, and G. Gao, 2020 Dynamic modeling and analysis for 6-DoF industrial robots. In 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS), pp. 247–252.

Marzouk, O. A., 2025 Coupled differential-algebraic equations framework for modeling six-degree-of-freedom flight dynamics of asymmetric fixed-wing aircraft. International Journal of Advanced and Applied Sciences 12: 30–51.

Mien, T. L., T. N. Tu, and V. V. An, 2024 Cascade PID control for altitude and angular position stabilization of 6-DoF UAV quadcopter. International Journal of Robotics and Control Systems 4: 814–831.

Oliveira, G. and G. Lima, 2020 Evaluation of scheduling algorithms for embedded FreeRTOS-based systems. In 2020 X Brazilian Symposium on Computing Systems Engineering (SBESC), pp. 1–8.

Pal, R., 2017 Comparison of the design of FIR and IIR filters for a given specification and removal of phase distortion from IIR filters. In 2017 International Conference on Advances in Computing, Communication and Control (ICAC3), pp. 1–3.

Prakosa, J. A., D. V. Samokhvalov, G. R. V. Ponce, and F. S. Al-Mahturi, 2019 Speed control of brushless DC motor for quadcopter drone ground test. In 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 644–648.

Praveen, V. and A. S. Pillai, 2016 Modeling and simulation of quadcopter using PID controller. International Journal of Control Theory and Applications 9: 7151–7158.

Suhail, S. A., M. A. Bazaz, and S. Hussain, 2022 Adaptive sliding mode-based active disturbance rejection control for a quadcopter. Transactions of the Institute of Measurement and Control 44: 3176–3190.

Suri, K., M. Mohta, and A. Rajawat, 2017 Design of a dual-band rectifier circuit for drone powering applications. In 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–4.

Åström, K. J. and T. Hägglund, 2006 Advanced PID Control. ISA – The Instrumentation, Systems, and Automation Society.

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