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Intelligent and Adaptive PID-Based Control Strategies for Autonomous UAV Systems: A Systematic Review and Comparative Analysis

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Abstract

The extensive use of UAVs for surveillance, logistics, agricultural, inspection, and disaster relief activities has made it necessary to design a more adaptable flight control system that can function effectively under a nonlinear and uncertain environment. Of all the types of controllers available, PID controllers continue to be extensively used owing to their ease of implementation and low cost. Unfortunately, the performance of traditional fixed gain PID controllers is significantly affected by changing parameters and disturbances. The current study involves an in-depth analysis of PID controlled unmanned aerial vehicles using PRISMA methodology. The pool of research papers included in the final analysis amounted to 94 publications sorted between 2020 and 2026. All these papers were grouped according to the nature of PID controllers used into classical, adaptive, robust, intelligent, and hybrid PID. The current research provides a comprehensive comparative overview of these papers on the basis of their ability to provide stability, tracking, disturbance rejection, robustness, and settling time. Adaptive and intelligent controllers are regarded as promising controllers that improve UAV performances due to their learning ability. Robust and hybrid PID-based controllers demonstrate the best performance in terms of disturbance rejection, stability, and robustness to uncertainty. Nevertheless, computational complexity, implementation features, scalability, and benchmarking criteria require further improvements. The possible directions of research include light control intelligence, reinforcement learning-based adaptation, explainable AI-driven control, and efficient real-time UAVs.

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Data Availability

No datasets were generated or analysed during the current study.

Abbreviations

UAV:

Unmanned aerial vehicle

PID:

Proportional–integral–derivative

(Kp):

Proportional gain

(Ki):

Integral gain

(Kd):

Derivative gain

(e(t)):

Error signal

(u(t)):

Control input

SMC:

Sliding mode Control

MPC:

Model predictive Control

H∞:

H-infinity control

GA:

Genetic algorithm

PSO:

Particle swarm optimization

DE:

Differential evolution

NSGA-II:

Non-dominated sorting genetic algorithm

NN:

Neural network

RNN:

Recurrent neural network

DRL:

Deep reinforcement learning

FOPID:

Fractional-order PID

RMSE:

Root mean square error

IAE:

Integral absolute error

References

  1. Aly, M., et al.: Real-flight-path tracking control design for quadrotor UAVs: a precision-guided approach. Automation. (2025). https://doi.org/10.3390/automation6040093

    Article  Google Scholar 

  2. Gong, H., et al.: Design and validation of a new tilting rotor VTOL drone: structural optimization, flight dynamics, and PID control. Sensors. (2025). https://doi.org/10.3390/s25113537

    Article  Google Scholar 

  3. Taş, G., Özdamar, M.: Estimation of PID parameters of BLDC motor system by using machine learning methods. Signal. Image Video Process. (2025). https://doi.org/10.1007/s11760-024-03714-z

    Article  Google Scholar 

  4. Ghane, R.G., Hassan, M.Y.: Wind disturbance rejection of a foldable quadrotor using nonlinear PID-based sliding mode control. Tikrit J. Eng. Sci. (2025). https://doi.org/10.25130/tjes.32.3.1

    Article  Google Scholar 

  5. Bai, Y., Li, K., Wang, G.: A hybrid quadrotor unmanned aerial vehicle control strategy using self-adaptive bald eagle search and fuzzy logic. Electronics (Switzerland). (2025). https://doi.org/10.3390/electronics14112112

  6. Zhang, H., Meng, W., Gao, Z., Liu, G., Zhang, J.: Optimization design and flight validation of pull-up control for air-deployed UAVs based on improved NSGA-II. Drones. (2025). https://doi.org/10.3390/drones9100679

    Article  Google Scholar 

  7. Sun, H., Liu, C., Tang, S., Suo, T.: Adaptive PID control of UAV variable-pitch propellers based on lightweight neural networks. IEEE Access. 14, 3386–3401 (2026). https://doi.org/10.1109/ACCESS.2025.3648468

    Article  Google Scholar 

  8. Laabousse, I., Haidoury, M., Idrissi, El Bouzekri El Idrissi, Y.: IJTPE journal performance analysis of advanced control techniques for fixed-wing unmanned aerial vehicles. Int. J. Tech. Phys. Probl. Eng. 65(17), 223–235 (2025). Available: https://www.iotpe.com

  9. Rinaldi, M., Moslehi, M., Guglieri, G., Primatesta, S.: PSO-based PID tuning for PMSM-quadrotor UAV system †. Eng. Proc. (2025). https://doi.org/10.3390/engproc2025090002

    Article  Google Scholar 

  10. Wang, H., et al.: Design of an attitude controller for an unmanned quadrotor aerial vehicle. ICIC Express Lett. 19(9), 995–1002 (2025). https://doi.org/10.24507/icicel.19.09.995

    Article  Google Scholar 

  11. Madebo, N.W.: Hybrid adaptive PID control strategy for UAVs using combined neural networks and fuzzy logic. PLoS One. (2025). https://doi.org/10.1371/journal.pone.0331036

    Article  Google Scholar 

  12. Kiss, B., Ballagi, Á., Kuczmann, M.: PID control and alternative control approaches in UAV systems. Int. J. Media Networks. 3(5), 01–06 (2025). https://doi.org/10.33140/IJMN.03.05.03

    Article  Google Scholar 

  13. Zhou, W., Zhou, L., Yuan, T., Chen, R., Liu, D.: Robust performance optimization of UAV dynamic systems using MPC-PID hybrid control. Sci. Rep. (2026). https://doi.org/10.1038/s41598-025-32436-6

    Article  Google Scholar 

  14. Sadi, M.A., Jamali, A., Abang Kamaruddin, A.M.N.: Optimizing UAV performance in turbulent environments using cascaded model predictive control algorithm and Pixhawk hardware. J. Brazilian Soc. Mech. Sci. Eng. (2025). https://doi.org/10.1007/s40430-025-05693-9

    Article  Google Scholar 

  15. Amal, C., Aicha, A., Mouna, B.H.: Optimizing drone control for wind turbine inspection. WSEAS Trans. Syst. Control. 20, 72–80 (2025). https://doi.org/10.37394/23203.2025.20.9

    Article  Google Scholar 

  16. Hwang, C.L., Li, C.Y., Wu, C.W.: Disturbance-observer-based finite-time following control with wind resistance capability for multiple GPS-denied UAVs. Int. J. Control Autom. Syst. 23(10), 3082–3099 (2025). https://doi.org/10.1007/s12555-025-0243-y

    Article  Google Scholar 

  17. Boubaker, S., Vacca, C., Rosales, C., Kamel, S., Alsubaei, F.S., Rossomando, F.: UAV formation for cargo transport by PID control with neural compensation. Mathematics. (2025). https://doi.org/10.3390/math13162650

    Article  Google Scholar 

  18. Xi, Y., Xu, Z., Zhou, F., Zeng, Z.: A hybrid controller based on feedforward compensator and PSO-aided SCPID for quadrotor UAV. Measur Control (United Kingdom). (2025). https://doi.org/10.1177/00202940251339152

    Article  Google Scholar 

  19. Khanzada, H.R., Maqsood, A., Basit, A.: Artificial intelligence in UAV flight controls: deep reinforcement learning based altitude-hold strategies for fixed-wing UAVs. IEEE Access. 13, 109670–109686 (2025). https://doi.org/10.1109/ACCESS.2025.3581966

    Article  Google Scholar 

  20. Hegde, N.T., Vaz, A.C., Nayak, C.G.: Closed loop performance analysis of classical PID and robust H-infinity controller for VTOL unmanned quad tiltrotor aerial vehicle. Int. J. Mech. 15, 211–222 (2021). https://doi.org/10.46300/9104.2021.15.25

    Article  Google Scholar 

  21. Tang, R., Tang, J., Aridas, N.K., Talip, M.S.A., Luen, L.C.: Research on RBF-PID hybrid drive control models for drone pesticide applications in tropical orchards. Geo-Spatial Inform. Sci. (2025). https://doi.org/10.1080/10095020.2025.2519374

    Article  Google Scholar 

  22. Yu, C., Liu, J., Xie, P., Xie, W.: A comprehensive review of UAV formation control from a mission-driven perspective. Drones. 10(4), 278 (2026). https://doi.org/10.3390/DRONES10040278

    Article  Google Scholar 

  23. Xie, F., Chai, Y., Chen, X., Zhang, K., Liu, Q.: Robust fractional-order PID controller design for fixed-wing UAVs through proximal-policy-optimization for disturbance rejection. Int. J. Mach. Learn. Cybern. 16(12), 10819–10834 (2025). https://doi.org/10.1007/s13042-025-02801-y

    Article  Google Scholar 

  24. Zekry, O.H., Ashry, M.M., Hafez, A.T., Attia, T.: Comprehensive analysis of nonlinear control strategies in trajectory tracking of Crazyflie Quadrotor. CEAS Aeronaut. J. (2025). https://doi.org/10.1007/s13272-025-00855-w

    Article  Google Scholar 

  25. Manalathody, A., Krishnan, K.S., Subramanian, J.A., Thangavel, S., Pushpangathan, J.V., Paranjothy, H.: Nonlinear and linear PID controllers-based hybrid flight control strategy for a quadcopter with slung load. IEEE Access. 13, 1–23 (2025). https://doi.org/10.1109/ACCESS.2025.3551622

    Article  Google Scholar 

  26. Karahan, M.: Robust backstepping control of a quadrotor unmanned aerial vehicle under colored noises. Comput. Mater. Continua. 82(1), 777–798 (2025). https://doi.org/10.32604/cmc.2024.059123

    Article  Google Scholar 

  27. Le, T.T.N., Vu, D.Q., Pham, X.T., Nguyen, V.M.: PID controller system for hexacopter using angular rate correction block. Sustainable Aviat. Part F1630, 297–303 (2026). https://doi.org/10.1007/978-3-031-71085-8_43

    Article  Google Scholar 

  28. Sun, L., Gu, M., Chen, T., Liu, X., Zheng, H.: Adaptive fuzzy PID search algorithm for global optimization with application to 3D UAV path planning. Appl. Soft Comput. (2026). https://doi.org/10.1016/j.asoc.2025.114201

    Article  Google Scholar 

  29. Lopez-Sanchez, I., Moreno-Valenzuela, J.: PID control of quadrotor UAVs: a survey. Annu. Rev. Control. 56, 100900 (2023). https://doi.org/10.1016/j.arcontrol.2023.100900

    Article  MathSciNet  Google Scholar 

  30. Cetinsaya, B., Reiners, D., Cruz-Neira, C.: From PID to swarms: a decade of advancements in drone control and path planning-a systematic review (2013–2023). Swarm Evol. Comput. 89, 101626 (2024). https://doi.org/10.1016/j.swevo.2024.101626

    Article  Google Scholar 

  31. Kim, J., Gadsden, S.A., Wilkerson, S.A.: A comprehensive survey of control strategies for autonomous quadrotors. Can. J. Electr. Comput. Eng. 43(1), 3–16 (2019). https://doi.org/10.1109/CJECE.2019.2920938

    Article  Google Scholar 

  32. Gedefaw, E.A., Abera, N.B., Abdissa, C.M.: A review of modeling and control techniques for unmanned aerial vehicles. Eng. Rep. 7(6), e70215 (2025). https://doi.org/10.1002/eng2.70215

    Article  Google Scholar 

  33. Wu, C.P., Hsieh, N.K., Chen, L.R., Balas, V.E.: Enhancing longitudinal flight stability of electric-powered micro unmanned aerial vehicles using fuzzy proportional-integral-derivative control. Int. J. Control Autom. Syst. 23(7), 2132–2141 (2025). https://doi.org/10.1007/s12555-024-0930-0

    Article  Google Scholar 

  34. Çağlar, F., Sadaghiani, K., Tunc, O., Altunok, İ., Sen, T.: Comprehensive review of atmospheric and environmental disturbances affecting unmanned aerial vehicles. Nat. Hazards Res. (2026). https://doi.org/10.1016/J.NHRES.2026.06.009

    Article  Google Scholar 

  35. Dagal, I., Erol, B., Harrison, A., Mbasso, W.F., Ibrahim, A.W.: Enhancing dynamic control and stability assessment of cessna 172 aircraft with a PID controller for new pilot trainees. Int. J. Aeronaut. Space Sci. 26(5), 2087–2104 (2025). https://doi.org/10.1007/s42405-025-00899-6

    Article  Google Scholar 

  36. Zhou, H.: Nonlinear PID controllers improved based on the quad rotor aircraft. Syst. Soft Comput. (2025). https://doi.org/10.1016/j.sasc.2025.200197

    Article  Google Scholar 

  37. Chen, R.: A comprehensive analysis of PID control applications in automation systems. Highlights Sci. Eng. Technol. 97, 126–132 (2024)

  38. Mubeena, M., Mullai Venthan, S., Nisha, M.S., Senthil Kumar, P., Vijayakumar, P., Rangasamy, G.: A survey of autopilot control systems: from classical PID to intelligent adaptive controllers. Int. J. Aeronautic Space Sci. 27(2), 1086–1111 (2025). https://doi.org/10.1007/S42405-025-01017-2/METRICS

    Article  Google Scholar 

  39. Zorain, M., Khan, F.S., Hasany, N., Din, Z.M.U., Gul, J.Z.: Deep reinforcement learning for UAV attitude control via adaptive gain optimization. Appl. Intell. 55(17), 1092 (2025). https://doi.org/10.1007/S10489-025-06978-1

    Article  Google Scholar 

  40. Singh, A.K., et al.: A comprehensive review of recent advances in intelligent controller development for smart irrigation systems. Discover Comput. 28(1), 239 (2025). https://doi.org/10.1007/S10791-025-09762-4

    Article  Google Scholar 

  41. Sahrir, N.H., Basri, M.A.M.: PSO–PID controller for quadcopter UAV: index performance comparison. Arab. J. Sci. Eng. 48(11), 15241–15255 (2023). https://doi.org/10.1007/S13369-023-08088-X

    Article  Google Scholar 

  42. Li, W., et al.: A sustainable crop protection through integrated technologies: UAV-based detection, real-time pesticide mixing, and adaptive spraying. Sci. Rep. 15(1), 35748 (2025). https://doi.org/10.1038/S41598-025-19473-X

    Article  Google Scholar 

  43. Yi, S.H., Shamsudin, S.S., Pairan, M.F.: Flight control system design for a rotorcraft-based unmanned aerial system using pole placement and LQR-based PID tuning techniques. J. Aeronaut. Astronaut. Aviat. 56(3), 615–636 (2024). https://doi.org/10.6125/JoAAA.202407_56(3).01

    Article  Google Scholar 

  44. İnan, M., Karci, A.: Effects of optimizing droplet distribution at particular heights and speeds using proportional-integral-derivative (PID) control algorithm in unmanned aerial vehicle (UAV) systems: a review. Tarim Bilimleri Dergisi. 31(3), 612–639 (2025). https://doi.org/10.15832/ankutbd.1611010

    Article  Google Scholar 

  45. Mejías, A.I.V., de la Torre, J.C., Marcos-Pérez, M., Escolano, A.Y., Bárcena-González, G.: A Matlab-based application for enhanced controller tuning in ArduSub ROVs: ATControlROV. SoftwareX. (2025). https://doi.org/10.1016/j.softx.2025.102189

    Article  Google Scholar 

  46. Al-Hamadani, S., Al-Darraji, I., Jerbi, H.: Improving barcode vision scanning process using a drone-based tracking PID controller for warehouse in industry 4.0. Al-Khwarizmi Eng. J. 21(2), 72–92 (2025). https://doi.org/10.22153/kej.2025.11.002

    Article  Google Scholar 

  47. Yang, Y.: Application and optimization of PID control in modern industrial systems. Appl. Comput. Eng. 195(1), 83–89 (2025). https://doi.org/10.54254/2755-2721/2025.gl28354

    Article  Google Scholar 

  48. Shi, M.: Application of PID control technology in unmanned aerial vehicles. Appl. Comput. Eng. 96(1), 24–30 (2024). https://doi.org/10.54254/2755-2721/96/20241327

    Article  Google Scholar 

  49. Noordin, A., Mohd Basri, M.A., Mohamed, Z.: Real-time implementation of an adaptive PID controller for the quadrotor MAV embedded flight control system. Aerospace. (2023). https://doi.org/10.3390/AEROSPACE10010059

    Article  Google Scholar 

  50. Vega, S., Vasquez-Guevara, M., Camacho, O.: A Comparison of adaptive PID, adaptive dual-PID and adaptive fractional PID controllers for a nonlinear system with variable parameters. In: Proceedings of the International Conference on Informatics in Control, Automation and Robotics, Science and Technology Publications, Lda, pp. 166–177. (2024). https://doi.org/10.5220/0013068100003822

  51. Arrieta, O., Campos, D., Rico-Azagra, J., Gil-Martínez, M., Rojas, J.D., Vilanova, R.: Model-based optimization approach for PID control of pitch–roll UAV orientation. Mathematics. (2023). https://doi.org/10.3390/math11153390

    Article  Google Scholar 

  52. Joseph, S.B., Dada, E.G., Abidemi, A., Oyewola, D.O., Khammas, B.M.: Metaheuristic Algorithms for PID Controller Parameters Tuning: Review, Approaches and Open Problems. Elsevier Ltd (2022). https://doi.org/10.1016/j.heliyon.2022.e09399

    Book  Google Scholar 

  53. Yoon, J., Kim, M., Bang, J., Kim, S., Doh, J.: Improvement of hovering stability for UAVs under crosswinds via evolutionary learning-based optimal PID control. J. Mech. Sci. Technol. 39(4), 2151–2162 (2025). https://doi.org/10.1007/s12206-025-0338-7

    Article  Google Scholar 

  54. Prasetiyo, E.E., Asnawi, R., Khairudin, M.: Optimization strategies for electric propulsion in UAVs: a review of technologies, control systems, and environmental impacts. Int. J. Mech. Eng. Rob. Res. (2025). https://doi.org/10.18178/ijmerr.14.6.657-666

    Article  Google Scholar 

  55. Gonzales-Zurita, O., Columba, E., Ortega, C.: Improving the control actions of a discrete FOPID controller by refining the integral term: application to a 1-DOF twin-rotor system. IET Control Theory Appl. (2025). https://doi.org/10.1049/cth2.70056

    Article  Google Scholar 

  56. Kiss, B., Ballagi, Á., Kuczmann, M.: Investigation of energy-efficient UAV control: analysis of PID and MPC performance †. Eng. Proc. (2025). https://doi.org/10.3390/engproc2025113040

    Article  Google Scholar 

  57. Yousfi, A.E., Alawi, Y.: Development and evaluation of drone based spraying system for precision agriculture application. Math. Modelling Eng. Probl. 12(1), 347–360 (2025). https://doi.org/10.18280/mmep.120134

    Article  Google Scholar 

  58. Canal, I.P., Reimbold, M.M.P., de Campos, M.: Ziegler–Nichols customization for quadrotor attitude control under empty and full loading conditions. CMES - Comput. Model. Eng. Sci. 125(1), 65–75 (2020). https://doi.org/10.32604/cmes.2020.010741

    Article  Google Scholar 

  59. Jaiswal, R., Prakash, O.: Classical and Modern gain estimation approach of PID controller for the pitch control of the RCTA aircraft. INCAS Bull. 14(1), 39–56 (2022). https://doi.org/10.13111/2066-8201.2022.14.1.4

    Article  Google Scholar 

  60. Maaruf, M., Abubakar, A.N., Gulzar, M.M.: Adaptive backstepping and sliding mode control of a quadrotor. J. Brazilian Soc. Mech. Sci. Eng. 46(11), 630 (2024). https://doi.org/10.1007/s40430-024-05188-z

    Article  Google Scholar 

  61. Maaruf, M., Abubakar, A.N., Gulzar, M.M., Bali, A.: Finite-time adaptive dynamic surface control of a quadrotor with input delay. Arab. J. Sci. Eng. 51, 10375–10391 (2026)

  62. Maaruf, M., Ahmad, S.S., Hamanah, W.M., Baraean, A.M., Alam, M.S., Abido, M.A., Shafiullah, M.: Advanced optimization methods for nonlinear backstepping controllers for quadrotor-slung load systems. IEEE Access. 13, 66607–66621 (2025)

    Article  Google Scholar 

  63. Chughtai, M.R., Anjum, M.L., Ahmad, I., Iqbal, M., Almakhles, D., Abdelrahim, M.: Trajectory tracking control design for quadcopter based on optimized integral super-twisting sliding mode technique. Eng. Sci. Technol. Int. J. (2025). https://doi.org/10.1016/j.jestch.2025.102222

    Article  Google Scholar 

  64. Boubakir, A., Souanef, T., Labiod, S., Whidborne, J.F.: A robust adaptive PID-like controller for quadrotor unmanned aerial vehicle systems. Aerospace. (2024). https://doi.org/10.3390/aerospace11120980

    Article  Google Scholar 

  65. Karahan, M., Kasnakoglu, C., Akay, A.N.: Robust backstepping control of a quadrotor UAV under pink noise and sinusoidal disturbance. Stud. Inf. Control. 32(2), 15–24 (2023). https://doi.org/10.24846/v32i2y202302

    Article  Google Scholar 

  66. Bianchi, D., Di Gennaro, S., Di Ferdinando, M., Lùa, A.: Robust control of UAV with disturbances and uncertainty estimation. Machines. (2023). https://doi.org/10.3390/machines11030352

    Article  Google Scholar 

  67. Siwek, M., Baranowski, L., Ładyżyńska-Kozdraś, E.: The application and optimisation of a neural network PID controller for trajectory tracking using UAVs. Sensors. (2024). https://doi.org/10.3390/s24248072

  68. Hailu, G.L., Yatayew, T.T.: Intelligent trajectory tracking in autonomous plantation robots using PSO-tuned nonlinear fuzzy PID, fuzzy PID, and conventional PID controllers. Sci. Rep. 15(1), 45357 (2025). https://doi.org/10.1038/S41598-025-30167-2

    Article  Google Scholar 

  69. Bujgoi, G., Sendrescu, D.: Tuning of PID controllers using reinforcement learning for nonlinear system control. Processes. (2025). https://doi.org/10.3390/pr13030735

    Article  Google Scholar 

  70. Chen, J., Shi, J., Sancaktar, C., Frey, J., Martius, G.: Offline vs. online learning in model-based RL: lessons for data collection strategies, (2025). Accessed: May 14, 2026. http://arxiv.org/abs/2509.05735

  71. Zhang, W., Xu, X.: Dynamic event-triggered control for unmanned aerial vehicle swarm adaptive target enclosing mission. Sensors. 26(2), 655 (2026). https://doi.org/10.3390/s26020655

    Article  MathSciNet  Google Scholar 

  72. Yoo, M.: Adaptive PID control based on laplace distribution for multi-environment temperature regulation in smart refrigeration systems. Energies (Basel). 19(2), 477 (2026). https://doi.org/10.3390/en19020477

    Article  Google Scholar 

  73. Melo, A.G., Andrade, F.A.A., Guedes, I.P., Carvalho, G.F., Zachi, A.R.L., Pinto, M.F.: Fuzzy Gain-scheduling PID for UAV position and altitude controllers. Sensors. 22(6), 2173 (2022). https://doi.org/10.3390/S22062173

    Article  Google Scholar 

  74. Abidin, Z., Budi, A.S., Tanesab, J.: Simulation-based study of MPC and GA-PID optimization for frequency regulation in a hybrid wind-diesel power system under load variation. Clean. Energy Syst. (2026). https://doi.org/10.1016/J.CLES.2025.100219

    Article  Google Scholar 

  75. Singh, A., Kumar, N., Badoni, M., Kumar, R., Joshi, B.P., Semwal, S.: Model predictive control (MPC) and proportional-integral-derivative (PID) controllers for load frequency control scheme. Suranaree J. Sci. Technol. 32(1), 1–13 (2025). https://doi.org/10.55766/SUJST6970

    Article  Google Scholar 

  76. Nhung, L.T.T., Van Minh, N., Quy, V.D., Tung, P.X.: Design and development of a fuzzy-PID controller with integrated wind estimation for UAV hexacopter stabilization. J. Aeronaut. Astronaut. Aviat. 57(5), 1265–1273 (2025). https://doi.org/10.6125/JoAAA.202505_57(5).08

    Article  Google Scholar 

  77. Surur, K., Kabir, I., Ahmad, G., Abido, M.A.: Optimal gain scheduling for fault-tolerant control of quadrotor UAV using genetic algorithm-based neural network. Arab. J. Sci. Eng. (2025). https://doi.org/10.1007/s13369-025-09994-y

    Article  Google Scholar 

  78. Chen, Q., Zhu, D., Liu, Z.: Attitude control of aerial and underwater vehicles using single-input FUZZY P + ID controller. Appl. Ocean. Res. (2021). https://doi.org/10.1016/j.apor.2020.102460

    Article  Google Scholar 

  79. Benbouali, A., Chabni, F., Taleb, R., Mansour, N.: Flight parameters improvement for an unmanned aerial vehicle using a lookup table based fuzzy PID controller. Indonesian J. Electr. Eng. Comput. Sci. 23(1), 171–178 (2021). https://doi.org/10.11591/ijeecs.v23.i1.pp171-178

    Article  Google Scholar 

  80. Rahani, F.F., Rosyady, P.A.: Quadrotor altitude control using recurrent neural network PID. Buletin Ilmiah Sarjana Teknik Elektro. 5(2), 279–290 (2023). https://doi.org/10.12928/biste.v5i2.8455

    Article  Google Scholar 

  81. Abualigah, L., Ekinci, S., Izci, D.: Aircraft pitch control via filtered proportional-integralderivative controller design using sinh cosh optimizer. Int. J. Rob. Control Syst. 4(2), 746–757 (2024). https://doi.org/10.31763/ijrcs.v4i2.1433

    Article  Google Scholar 

  82. Sahrir, N.H., Basri, M.A.M.: Intelligent PID controller based on neural network for AI-driven control quadcopter UAV. Int. J. Rob. Control Syst. 4(2), 691–708 (2024). https://doi.org/10.31763/ijrcs.v4i2.1374

    Article  Google Scholar 

  83. Pairan, M.F., Shamsudin, S.S., Yaakub, M.F.: Autotuning PID controllers for quadplane hybrid UAV using differential evolution algorithm. J. Aeronaut. Astronaut. Aviat. 56(1), 341–356 (2024). https://doi.org/10.6125/JoAAA.202403_56(1S).23

    Article  Google Scholar 

  84. Sun, H., Liu, C., Tang, S., Suo, T.: Adaptive control for UAV speed tracking with multi-delay compensation using smith predictor and fuzzy PID tuning. Electron. (Switzerland). (2025). https://doi.org/10.3390/electronics14163288

    Article  Google Scholar 

  85. Li, G., et al.: Research on synchronized control strategy of drone swarm garage side-pulling platform. IEEE Access. 13, 91795–91807 (2025). https://doi.org/10.1109/ACCESS.2025.3568850

    Article  Google Scholar 

  86. Ren, Y., Zhu, F., Sui, S., Yi, Z., Chen, K.: Enhancing quadrotor control robustness with multi-proportional–integral–derivative self-attention-guided deep reinforcement learning. Drones. (2024). https://doi.org/10.3390/drones8070315

    Article  Google Scholar 

  87. Liu, Y., Yan, X., Yan, F., Xu, Z., Shang, W.: Sliding-mode PID control of UAV based on particle swarm parameter tuning. Comput. Mater. Continua. 63(1), 469–487 (2020). https://doi.org/10.32604/cmc.2020.05746

    Article  Google Scholar 

  88. Leal, I.S., Abeykoon, C., Perera, Y.S.: Design, simulation, analysis and optimization of pid and fuzzy based control systems for a quadcopter. Electron. (Switzerland). (2021). https://doi.org/10.3390/electronics10182218

    Article  Google Scholar 

  89. AL-Jarrah, R.A.: Hybrid fuzzy-PID closed loop to regulate quadcopter system. Int. J. Mech. Eng. Robot Res. 10(9), 469–477 (2021). https://doi.org/10.18178/ijmerr.10.9.469-477

    Article  Google Scholar 

  90. Fadel, M.Z.: Hybrid control algorithm sliding mode-PID for an electrohydraulic servo actuator system based on particle swarm optimization technique. J. Europeen des Systemes Automatises. 56(1), 153–163 (2023). https://doi.org/10.18280/jesa.560119

    Article  Google Scholar 

  91. Omar, M.H.T., Hamdy, R.A., Kotb, H.: A hybrid fuzzy-PIDD2 control strategy for coordinated LFC and AVR in renewable-integrated multi-area power systems. Sci. Rep. 16(1), 3741 (2026). https://doi.org/10.1038/s41598-025-34481-7

    Article  Google Scholar 

  92. Bhandari, P., et al.: A hybrid RTDA–PID control strategy for BLDC motors using FOPDT based approximation and SBOA optimization. Eng. Res. Express. 7(3), 035365 (2025). https://doi.org/10.1088/2631-8695/AE0095

    Article  Google Scholar 

  93. Hu, N.: The limitations of traditional PID controllers and modern optimization methods. Appl. Comput. Eng. 147(1), 238–244 (2025). https://doi.org/10.54254/2755-2721/2025.22912

    Article  Google Scholar 

  94. Meda-Campaña, J.A., Lizardo-Parra, I.I., García-Hernández, J.C., Escobedo-Alva, J.O., Páramo-Carranza, L.A., Tapia-Herrera, R.: Energy-efficient hybrid PID control with exponential trajectories for smooth setpoint transitions: applications in robotics and aeronautics. Appl. Sci. 15(13), 7223 (2025). https://doi.org/10.3390/APP15137223

    Article  Google Scholar 

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Acknowledgements

The authors wish to thank Lovely Professional University, Phagwara for all support.

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  1. Department of Aerospace Engineering, School of Mechanical Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India

    Rohit Kumar & Amit Kumar Thakur

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Rohit Kumar: drafting-original writing, methodology, software, investigation, conceptual design, visualization, validation, project coordination, formal analysis, data handling, conceptual input. Amit Kumar Thakur: drafting-original writing, methodology, software, investigation, review -editing, supervision, conceptual support.

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Kumar, R., Thakur, A.K. Intelligent and Adaptive PID-Based Control Strategies for Autonomous UAV Systems: A Systematic Review and Comparative Analysis. Aerotec. Missili Spaz. 105, 1085–1115 (2026). https://doi.org/10.1007/s42496-026-00330-7

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