An Adaptive Nonlinear Control Approach for Quadrotor Uav Trajectory Tracking

Quadrotor unmanned aerial vehicles are nonlinear and underactuated systems whose control performance is strongly affected by model uncertainties and external disturbances. This paper presents an adaptive nonlinear control approach for quadrotor UAV trajectory tracking. The proposed strategy is developed from an adaptive fuzzy control framework for a general second-order nonlinear system, in which the control signal is generated through a Sugeno-type fuzzy structure and the controller parameters are updated online according to adaptive laws derived from the Lyapunov stability criterion. The resulting control architecture is then extended to the position and attitude subsystems of the quadrotor through six control channels with corresponding adaptive parameter update laws. Simulation results show that the quadrotor can follow the prescribed trajectory with stable position and attitude responses, while the tracking errors remain bounded and decrease toward a small neighborhood of zero. The results indicate that the proposed adaptive nonlinear control structure provides good adaptability and satisfactory tracking performance under the considered operating conditions. Therefore, the proposed approach is a feasible solution for quadrotor UAV control in the presence of uncertainties and disturbances.

Adaptive nonlinear control; Quadrotor UAV; Trajectory tracking; Adaptive fuzzy approximation; Lyapunov stability

1. M. Maaruf, M. S. Mahmoud, and M. A. Al-Ma’arif, "A survey of control methods for quadrotor UAV," Int. J. Robot. Control Syst., Vol. 2, No. 4, pp. 652–665, 2022. [Google Scholar] [Crossref]

2. S. H. Derrouaoui, Y. Bouzid, A. Belmouhoub, M. Guiatni, H. Siguerdidjane, "Recent Developments and Trends in Unconventional UAVs Control: A Review," J Int Rob Syst, 109, 68, 2023. [Google Scholar] [Crossref]

3. S. Khatoon, D. Gupta and L. K. Das, "PID & LQR control for a quadrotor: Modeling and simulation," 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Delhi, India, 2014, pp. 796-802. [Google Scholar] [Crossref]

4. Gedefaw, E. A. et al, “ Improved trajectory tracking control using fuzzy PID surface for quadrotor UAV,” PLoS ONE, 19(11), pp. 1-36, 2024. [Google Scholar] [Crossref]

5. Yasmine, Z. “Adaptive fuzzy logic control of quadrotor UAV,” International Journal of Robotics and Control Systems, Vol. 4, No. 4, pp. 2095-2118, 2024. [Google Scholar] [Crossref]

6. C. Peng, Y. Tian, et al., "ADRC trajectory tracking control based on PSO algorithm for a quad-rotor," In: 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), pp. 800–805, 2013. [Google Scholar] [Crossref]

7. P. J. D. de Oliveira Evald, V. M. Aoki, et al., "A review on quadrotor attitude control strategies," Int. J. Intell. Robot. Appl., pp. 1–21, 2024. [Google Scholar] [Crossref]

8. Y. Huang, J. Chen, and H. Zhang, "Sliding mode control for quadrotor UAVs: A review of recent advancements," Journal of Intelligent & Robotic Systems, Vol. 94, pp. 211–225, 2019. [Google Scholar] [Crossref]

9. Ren, Z. et al,” Adaptive fault-tolerant control for quadrotor UAV based on fuzzy gain sliding mode,” [Google Scholar] [Crossref]

10. Journal of Northeastern University, pp209-216, 2024 [Google Scholar] [Crossref]

11. Dou, J. et al. “ Trajectory tracking and formation control for quadrotor UAVs,” Scientific Reports, 15, 24039, pp. 1-30, 2025. [Google Scholar] [Crossref]

12. T. Dierks and S. Jagannathan, "Output feedback control of a quadrotor UAV using neural networks," IEEE Trans. Neural Netw., Vol. 21, No. 1, pp. 50–66, 2010 [Google Scholar] [Crossref]

13. Z. Xu, T. Yan, S. X. Yang, and S. A. Gadsden, “Bioinspired backstepping sliding mode control and adaptive sliding innovation filter of quadrotor unmanned aerial vehicles,” Biomimetic Intelligence and Robotics, vol.3, no. 3, 2023. [Google Scholar] [Crossref]

14. Li, C. et al, “Adaptive fuzzy tracking control for quadrotor UAV with disturbance observer,” Aerospace Science and Technology, vol. 128, 107784, pp. 1-11, 2022. [Google Scholar] [Crossref]

15. Zhou, L. et al, “ Fuzzy adaptive backstepping trajectory tracking control for quadrotor systems,” International Journal of Fuzzy Systems, 2024 [Google Scholar] [Crossref]

16. Wei, P. et al, ” Adaptive backstepping neural control for quadrotor UAVs under disturbances,” Expert Systems with Applications, 296, pp. 1-24, 2025 [Google Scholar] [Crossref]

17. Tran, V. P. et al.” Robust fuzzy Q-learning tracking control for quadrotor systems,” IEEE Robotics research. 2022 [Google Scholar] [Crossref]

18. B. Erginer and E. Altug, "Design and implementation of a hybrid fuzzy logic controller for a quadrotor VTOL vehicle," International Journal of Control, Automation and Systems, Vol. 10, pp. 61–70, 2012. [Google Scholar] [Crossref]

19. L. A. Zadeh, "Fuzzy sets," Information and Control, Vol. 8, No. 3, pp. 338–353, 1965. [Google Scholar] [Crossref]

20. L. X. Wang, "Stable adaptive fuzzy control of nonlinear systems," IEEE Trans. Fuzzy Syst., Vol. 1, No. 2, pp. 146–155, 1993. [Google Scholar] [Crossref]

21. S. Labiod and T. M. Guerra, "Adaptive fuzzy control of a class of SISO non-affine nonlinear systems," Fuzzy Sets and Systems, Vol. 158, No. 10, pp. 1126–1137, 2007. [Google Scholar] [Crossref]

22. J. S. R. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE Trans. Syst., Man, Cyber. [Google Scholar] [Crossref]

• Performance Analysis of Silicon PV Array Using Infrared Thermography and Detecting Temperature Non-Uniformities
• Dynamic Capabilities and Digital Transformation Performance: The Role of Organizational Agility in the Public Sector
• Comparative Effectiveness of Neural Mobilization Versus Positional Release Technique in Cervical Radiculopathy – An Experimental Study
• Soil Moisture Mapping of Solano Nueva Vizcaya: A Comparison and Review
• Silent Assistance for Emergencies (SAFE): A Mobile-Based Emergency Reporting Application for the Deaf and Mute Community