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A Survey of Path Following Control Strategies for UAVs Focused on Quadrotors

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Abstract

The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.

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References

  1. Aguiar, A.P., Hespanha, J.P.: Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty. IEEE Trans. Autom. Control 52(8), 1362–1379 (2007). https://doi.org/10.1109/TAC.2007.902731

    Article  MathSciNet  MATH  Google Scholar 

  2. Aguiar, A.P., Hespanha, J.P., Kokotovic, P.V.: Performance limitations in reference tracking and path following for nonlinear systems. Automatica 44(3), 598–610 (2008). https://doi.org/10.1016/j.automatica.2007.06.030

    Article  MathSciNet  MATH  Google Scholar 

  3. Akhtar, A., Waslander, S.L., Nielsen, C.: Path following for a quadrotor using dynamic extension and transverse feedback linearization. In: 2012 IEEE 51st Annual Conference on Decision and Control (CDC), IEEE Conference on Decision and Control, pp. 3551–3556 (2012)

  4. Akhtar, A., Waslander, S.L., Nielsen, C.: Fault Tolerant Path Following for a Quadrotor. In: 2013 IEEE 52nd Annual Conference on Decision and Control (CDC), Conference on Decision and Control, pp. 847–852 (2013)

  5. Akkinapalli, V.S., Niermeyer, P., Lohmann, B., Holzapfel, F.: Adaptive nonlinear design plant uncertainty cancellation for a multirotor. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1102–1110 (2016). https://doi.org/10.1109/ICUAS.2016.7502555

  6. Alexis, K., Papachristos, C., Siegwart, R., Tzes, A.: Robust model predictive flight control of unmanned rotorcrafts. J. Intell. Robot. Syst. 81(3), 443–469 (2016). https://doi.org/10.1007/s10846-015-0238-7

    Article  Google Scholar 

  7. Ali, S.U., Samar, R., Shah, M.Z., Bhatti, A.I., Munawar, K., Al-Sggaf, U.M.: Lateral guidance and control of UAVs using second-order sliding modes. Aerosp. Sci. Technol. 49, 88–100 (2016). https://doi.org/10.1016/j.ast.2015.11.033

    Article  Google Scholar 

  8. Ambrosino, G., Ariola, M., Ciniglio, U., Corraro, F., De Lellis, E., Pironti, A.: Path generation and tracking in 3-D for UAVs. IEEE Trans. Control Syst. Technol. 17(4), 980–988 (2009). https://doi.org/10.1109/TCST.2009.2014359

    Article  Google Scholar 

  9. Amidi, O., Thorpe, C.: Integrated mobile robot control. In: Mobile Robots V., vol. 1388 (1991) https://doi.org/10.1117/12.25494

  10. Amin, R., Aijun, L., Shamshirband, S.: A review of quadrotor UAV: control methodologies and performance evaluation. Int. J. Autom. Control. 10(2), 87–103 (2016). https://doi.org/10.1504/IJAAC.2016.076453

    Article  Google Scholar 

  11. Baca, T., Loianno, G., Saska, M.: Embedded model predictive control of unmanned micro aerial vehicles. In: 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 992–997 (2016). https://doi.org/10.1109/MMAR.2016.7575273

  12. Başçi, A., Can, K., Orman, K., Derdiyok, A.: Trajectory tracking control of a four rotor unmanned aerial vehicle based on continuous sliding mode controller. Elektronika Ir ElektrotechnikA 23(3), 12–19 (2017)

    Article  Google Scholar 

  13. Bouabdallah, S., Murrieri, P., Siegwart, R.: Design and Control of an Indoor Micro Quadrotor. In: 2004 IEEE International Conference on Robotics and Automation (ICRA), vol. 5, pp. 4393–4398 (2004). https://doi.org/10.1109/ROBOT.2004.1302409

  14. Byrnes, C., Isidori, A.: Asymptotic stabilization of minimum phase nonlinear-systems. IEEE Trans. Autom. Control 36(10), 1122–1137 (1991). https://doi.org/10.1109/9.90226

    Article  MathSciNet  MATH  Google Scholar 

  15. Cabecinhas, D., Cunha, R., Silvestre, C.: Rotorcraft path following control for extended flight envelope coverage. In: Proceedings of the 48th IEEE Conference on Decision and Control, 2009 Held Jointly with the 2009 28th Chinese Control Conference (CDC/CCC 2009), IEEE Conference on Decision and Control, pp. 3460–3465 (2009). https://doi.org/10.1109/CDC.2009.5400665

  16. Cabecinhas, D., Cunha, R., Silvestre, C.: A globally stabilizing path following controller for rotorcraft with wind disturbance rejection. IEEE Trans. Control Syst. Technol. 23(2), 708–714 (2015). https://doi.org/10.1109/TCST.2014.2326820

    Article  Google Scholar 

  17. Camacho, E., Bordons, C.: Model Predictive Control. Advanced Textbooks in Control and Signal Processing. Springer, London (2004). https://books.google.es/books?id=Sc1H3f3E8CQC

    MATH  Google Scholar 

  18. Chen, H., Chang, K., Agate, C.S.: UAV path planning with tangent-plus-Lyapunov vector field guidance and obstacle avoidance. IEEE Trans. Aerosp. Electron. Syst. 49(2), 840–856 (2013)

    Article  Google Scholar 

  19. Chen, Y., Liang, J., Wang, C., Zhang, Y.: Combined of Lyapunov-stable and active disturbance rejection control for the path following of a small unmanned aerial vehicle. Int. J. Adv. Robot. Syst. 14(2). https://doi.org/10.1177/1729881417699150 (2017)

  20. Chen, Y., Liang, J., Wang, C., Zhang, Y., Wang, T., Xue, C.: Planar smooth path guidance law for a small unmanned aerial vehicle with parameter tuned by fuzzy logic. Journal of Control Science and Engineering 2017, 11 (2017). https://doi.org/10.1155/2017/6712602

    Article  MathSciNet  MATH  Google Scholar 

  21. Chen, Y., Yu, J., Mei, Y., Wang, Y., Su, X.: Modified central force optimization (MCFO) algorithm for 3D UAV path planning. Neurocomputing 171, 878–888 (2016). https://doi.org/10.1016/j.neucom.2015.07.044

    Article  Google Scholar 

  22. Chen, Y., Yu, J., Su, X., Luo, G.: Path planning for multi-UAV formation. Journal of Intelligent & Robotic Systems 77(1, SI), 229–246 (2015). https://doi.org/10.1007/s10846-014-0077-y

    Article  Google Scholar 

  23. Cho, N., Kim, Y., Park, S.: Three-dimensional nonlinear differential geometric path-following guidance law. J. Guid. Control Dynam. 38(12), 2366–2385 (2015). https://doi.org/10.2514/1.G001060

    Article  Google Scholar 

  24. Choi, S., Kim, S., Jin Kim, H.: Inverse reinforcement learning control for trajectory tracking of a multirotor UAV. Int. J. Control. Autom. Syst. 15(4), 1826–1834 (2017). https://doi.org/10.1007/s12555-015-0483-3

    Article  Google Scholar 

  25. Cichella, V., Choe, R., Mehdi, S.B., Xargay, E., Hovakimyan, N., Kaminer, I., Dobrokhodov, V.: A 3d path-following approach for a multirotor uav on so(3). In: IFAC Proceedings Volumes, vol. 46, pp. 13–18 (2013). https://doi.org/10.3182/20131120-3-FR-4045.00039

  26. Cichella, V., Kaminer, I., Dobrokhodov, V., Xargay, E., Choe, R., Hovakimyan, N., Aguiar, A.P., Pascoal, A.M.: Cooperative path following of multiple multirotors over time-varying networks. IEEE Trans. Autom. Sci. Eng. 12(3), 945–957 (2015). https://doi.org/10.1109/TASE.2015.2406758

    Article  Google Scholar 

  27. Dadkhah, N., Mettler, B.: Control system design and evaluation for robust autonomous rotorcraft guidance. Control. Eng. Pract. 21(11), 1488–1506 (2013). https://doi.org/10.1016/j.conengprac.2013.04.011

    Article  Google Scholar 

  28. Dubins, L.: On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. Am. J. Math. 79(3), 497–516 (1957). https://doi.org/10.2307/2372560

    Article  MathSciNet  MATH  Google Scholar 

  29. Edwards, C., Spurgeon, S.: Sliding Mode Control: Theory And Applications. Series in Systems and Control. Taylor & Francis, New York (1998). https://books.google.es/books?id=uH2RJhIPsiYC

    Book  Google Scholar 

  30. Escareño, J., Salazar, S., Romero, H., Lozano, R.: Trajectory control of a quadrotor subject to 2d wind disturbances. Journal of Intelligent Robotic & Systems 70(1), 51–63 (2013). https://doi.org/10.1007/s10846-012-9734-1

    Article  Google Scholar 

  31. Faulwasser, T., Findeisen, R.: Nonlinear model predictive control for constrained output path following. IEEE Trans. Autom. Control 61(4), 1026–1039 (2016). https://doi.org/10.1109/TAC.2015.2466911

    Article  MathSciNet  MATH  Google Scholar 

  32. Gandolfo, D.C., Salinas, L.R., Toibero, J.M., Brandao, A.: Path following for unmanned helicopter: an approach on energy autonomy improvement. Information Technology and Control 45(1), 86–98 (2016). https://doi.org/10.5755/j01.itc.45.1.12413

    Article  Google Scholar 

  33. García, C.E., Prett, D.M., Morari, M.: Model predictive control: Theory and practice-a survey. Automatica 25(3), 335–348 (1989). https://doi.org/10.1016/0005-1098(89)90002-2

    Article  MATH  Google Scholar 

  34. Gautam, A., Sujit, P.B., Saripalli, S.: Application of guidance laws to quadrotor landing. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 372–379 (2015)

  35. Gerlach, A.R., Kingston, D., Walker, B.K.: UAV navigation using predictive vector field control. In: 2014 American Control Conference, pp. 4907–4912. https://doi.org/10.1109/ACC.2014.6859082 (2014)

  36. Gu, N., Wang, D., Liu, L., Zhang, B., Peng, Z.: Adaptive line-of-sight guidance law for synchronized path-following of under-actuated unmanned surface vehicles based on low-frequency learning. In: 2017 36th Chinese Control Conference (CCC), pp. 6632–6637 (2017). https://doi.org/10.23919/ChiCC.2017.8028408

  37. Hamada, Y., Tsukamoto, T., Ishimoto, S.: Receding horizon guidance of a small unmanned aerial vehicle for planar reference path following. Aerosp. Sci. Technol. 77, 129–137 (2018). https://doi.org/10.1016/j.ast.2018.02.039

    Article  Google Scholar 

  38. Hartman, D., Landis, K., Mehrer, M., Moreno, S., Kim, J.: Quadcopter dynamic modeling and simulation (Quad-Sim) (2014). https://github.com/dch33/Quad-Sim

  39. Heng, X., Cabecinhas, D., Cunha, R., Silvestre, C., Qingsong, X.: A trajectory tracking Lqr controller for a Quadrotor: design and experimental evaluation. In: TENCON 2015 - 2015 IEEE Region 10 Conference, pp. 1–7. https://doi.org/10.1109/TENCON.2015.7372729 (2015)

  40. Jung, D., Ratti, J., Tsiotras, P.: Real-time implementation and validation of a new hierarchical path planning scheme of UAVs via hardware-in-the-loop simulation. J. Intell. Robot. Syst. 54(1-3, SI), 163–181 (2009)

    Article  Google Scholar 

  41. Jung, W., Lim, S., Lee, D., Bang, H.: Unmanned aircraft vector field path following with arrival angle control. Journal of Intelligent Robotic & Systems 84(1), 311–325 (2016). https://doi.org/10.1007/s10846-016-0332-5

    Article  Google Scholar 

  42. Kaminer, I., Yakimenko, O., Pascoal, A., Ghabcheloo, R.: Path generation, path following and coordinated control for time-critical missions of multiple UAVs. In: 2006 American Control Conference, Proceedings of the American Control Conference. https://doi.org/10.1109/ACC.2006.1657498, vol. 1–12, p 4906+ (2006)

  43. Klausen, K., Fossen, T.I., Johansen, T.A., Aguiar, A.P.: Cooperative path-following for multirotor UAVs with a suspended payload. In: 2015 IEEE Conference on Control and Applications (CCA 2015), pp. 1354–1360 (2015)

  44. Kokotovic, P.V., Sussmann, H.J.: A positive real condition for global stabilization of nonlinear systems. Syst. Control Lett. 13(2), 125–133 (1989)

    Article  MathSciNet  Google Scholar 

  45. Kothari, M., Postlethwaite, I., Gu, D.W.: A suboptimal path planning algorithm using rapidly-exploring random trees. International Journal of Aerospace Innovations 2, 93–104 (2009)

    Google Scholar 

  46. Kukreti, S., Kumar, M., Cohen, K.: Genetically tuned Lqr based path following for UAVs under wind disturbance. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 267–274. https://doi.org/10.1109/ICUAS.2016.7502620 (2016)

  47. Lee, H., Kim, H.J.: Trajectory tracking control of multirotors from modelling to experiments: a survey. Int. J. Control. Autom. Syst. 15(1), 281–292 (2017). https://doi.org/10.1007/s12555-015-0289-3

    Article  Google Scholar 

  48. Li, L., Sun, L., Jin, J.: Survey of advances in control algorithms of quadrotor unmanned aerial vehicle. In: 2015 IEEE 16th International Conference on Communication Technology (ICCT), pp. 107–111 (2015)

  49. Liang, Y., Jia, Y.: Combined vector field approach for 2D and 3D arbitrary twice differentiable curved path following with constrained UAVs. Journal of Intelligent Robotic & Systems 83(1), 133–160 (2016). https://doi.org/10.1007/s10846-015-0308-x

    Article  Google Scholar 

  50. Liu, P., Chen, A.Y., Huang, Y.N., Han, J.Y., Lai, J.S., Kang, S.C., Wu, T.H., Wen, M.C., Tsai, M.H.: A review of rotorcraft unmanned aerial vehicle (UAV) developments and applications in civil engineering. Smart Struct. Syst. 13(6), 1065–1094 (2014)

    Article  Google Scholar 

  51. Lou, W., Guo, X.: Adaptive trajectory tracking control using reinforcement learning for quadrotor. Int. J. Adv. Robot. Syst. 13(1), 38 (2016). https://doi.org/10.5772/62128

    Article  Google Scholar 

  52. Mahony, R., Kumar, V., Corke, P.: Multirotor aerial vehicles: modeling, estimation, and control of quadrotor. IEEE Robot. Autom. Mag. 19(3), 20–32 (2012). https://doi.org/10.1109/MRA.2012.2206474

    Article  Google Scholar 

  53. Manjunath, A., Mehrok, P., Sharma, R., Ratnoo, A.: Application of virtual target based guidance laws to path following of a quadrotor UAV. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 252–260. https://doi.org/10.1109/ICUAS.2016.7502565 (2016)

  54. de Marina, H.G., Kapitanyuk, Y.A., Bronz, M., Hattenberger, G., Cao, M.: Guidance algorithm for smooth trajectory tracking of a fixed wing UAV flying in wind flows. In: 2017 IEEE International Conference on Tobotics and Automation (ICRA), pp. 5740–5745 (2017)

  55. Martinsen, A.B., Lekkas, A.M.: Curved path following with deep reinforcement learning: Results from three vessel models. OCEANS 2018 MTS/IEEE Charleston, pp. 1–8 (2018)

  56. Mayne, D., Rawlings, J., Rao, C., Scokaert, P.: Constrained model predictive control: Stability and optimality. Automatica 36(6), 789–814 (2000). https://doi.org/10.1016/S0005-1098(99)00214-9

    Article  MathSciNet  MATH  Google Scholar 

  57. Mendoza-Soto, J.L., Corona-Sanchez, J.J., Rodriguez-Cortes, H.: Quadcopter path following control. a maneuvering approach. Journal of Intelligent & Robotic Systems. https://doi.org/10.1007/s10846-018-0801-0 (2018)

  58. Miao, C.X., Fang, J.C.: An adaptive three-dimensional nonlinear path following method for a fix-wing micro aerial vehicle. Int. J. Adv. Robot. Syst. 9, 1 (2012)

    Article  Google Scholar 

  59. Micaelli, A., Samson, C., Robotique, P., Icare, P.: Trajectory tracking for unicycle-type and two-steering-wheels mobile robots. In: IFAC Proceedings Volumes, vol. 27, pp. 249–256 (1994)

  60. Nelson, D.R., Barber, D.B., McLain, T.W., Beard, R.W.: Vector field path following for miniature air vehicles. IEEE Transactions on Robotics 23(3), 519–529 (2007). https://doi.org/10.1109/TRO.2007.898976

    Article  Google Scholar 

  61. Nie, C., Zheng, Z., Cai, Z.: Three-dimensional path-following control of a robotic airship with reinforcement learning. In: 2019 International Journal of Aerospace Engineering (2019)

  62. Niermeyer, P., Akkinapalli, V.S., Pak, M., Holzapfel, F., Lohmann, B.: Geometric path following control for multirotor vehicles using nonlinear model predictive control and 3D spline paths. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 126–134. https://doi.org/10.1109/ICUAS.2016.7502541 (2016)

  63. Ollero, A., Heredia, G.: Stability analysis of mobile robot path tracking. In: Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Interaction and Cooperative Robots, vol. 3, pp. 461–466 (1995). https://doi.org/10.1109/IROS.1995.525925

  64. Ozbek, N.S., Onkol, M., Efe, M.O.: Feedback control strategies for quadrotor-type aerial robots: a survey. Trans. Inst. Meas. Control. 38(5, SI), 529–554 (2016). https://doi.org/10.1177/0142331215608427

    Article  Google Scholar 

  65. Park, S., Deyst, J., How, J.: Performance and lyapunov stability of a nonlinear path-following guidance method. J. Guid. Control Dynam. 30, 1718–1728 (2007)

    Article  Google Scholar 

  66. Raffo, G.V., Ortega, M.G., Rubio, F.R.: Backstepping/nonlinear h-infinity control for path tracking of a quadrotor unmanned aerial vehicle. In: 2008 American Control Conference, vol. 1–12, pp. 3356–3361 (2008)

  67. Ratnoo, A., Hayoun, S., Granot, A., Shima, T.: Path following using trajectory shaping guidance. J. Guid. Control Dynam. 38, 106–116 (2015). https://doi.org/10.2514/1.G000300

    Article  Google Scholar 

  68. Roza, A., Maggiore, M.: Path following controller for a quadrotor helicopter. In: 2012 American Control Conference (ACC), pp. 4655–4660 (2012)

  69. Rucco, A., Aguiar, A.P., Hauser, J.: Trajectory optimization for constrained UAVs: a virtual target vehicle approach. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 236–245. https://doi.org/10.1109/ICUAS.2015.7152296 (2015)

  70. Rucco, A., Aguiar, A.P., Pereira, F.L., de Sousa, J.B.: A Predictive Path-Following Approach for Fixed-Wing Unmanned Aerial Vehicles in Presence of Wind Disturbances, vol. 427, pp. 623–634. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27146-0_48

    Google Scholar 

  71. Rysdyk, R.: UAV path following for target observation in wind. J. Guid. Control Dynam. 29(5), 1092–1100 (2006). https://doi.org/10.2514/1.19101

    Article  Google Scholar 

  72. Shah, M.Z., Samar, R., Bhatti, A.I.: Lateral track control of UAVs using the sliding mode approach: from design to flight testing. Trans. Inst. Meas. Control. 37(4), 457–474 (2015). https://doi.org/10.1177/0142331214543093

    Article  Google Scholar 

  73. Shen, H., Guo, C.: Path-following control of underactuated ships using actor-critic reinforcement learning with Mlp neural networks. In: 2016 Sixth International Conference on Information Science and Technology (ICIST), pp. 317–321. https://doi.org/10.1109/ICIST.2016.7483431 (2016)

  74. Singh, S., Padhi, R.: Automatic path planning and control design for autonomous landing of UAVs using dynamic inversion. In: 2009 American Control Conference, Vols 1–9, Proceedings of the American Control Conference, pp. 2409–2414 (2009)

  75. Sontag, E.D., Sussmann, H.J.: Further comments on the stabilizability of the angular velocity of a rigid body. Syst. Control Lett. 12(3), 213–217 (1989)

    Article  MathSciNet  Google Scholar 

  76. Stevšić, S., Nägeli, T., Alonso-Mora, J., Hilliges, O.: Sample efficient learning of path following and obstacle avoidance behavior for quadrotors. IEEE Robotics and Automation Letters 3(4), 3852–3859 (2018). https://doi.org/10.1109/LRA.2018.2856922

    Article  Google Scholar 

  77. Su, S.: Path following control of non-minimum phase VTOL aircraft via minimum distance projection method. In: 26th Chinese Control and Decision Conference (2014 CCDC), Chinese Control and Decision Conference, pp. 708–712 (2014)

  78. Sujit, P.B., Saripalli, S., Sousa, J.B.: Unmanned aerial vehicle path following: a survey and analysis of algorithms for fixed-wing unmanned aerial vehicless. IEEE Control Systems 34(1), 42–59 (2014). https://doi.org/10.1109/MCS.2013.2287568

    Article  MathSciNet  Google Scholar 

  79. Valencia, D.R., Kim, D.: Trajectory tracking control for multiple quadrotors based on a neurobiological-inspired system. 2019 Third IEEE International Conference on Robotic Computing (IRC) pp. 465–470 (2019)

  80. Van Loock, W., Pipeleers, G., Diehl, M., De Schutter, J., Swevers, J.: Optimal path following for differentially flat robotic systems through a geometric problem formulation. IEEE Trans. Robot. 30(4), 980–985 (2014). https://doi.org/10.1109/TRO.2014.2305493

    Article  Google Scholar 

  81. Wang, C., Song, B., Huang, P., Tang, C.: Trajectory tracking control for quadrotor robot subject to payload variation and wind gust disturbance. Journal of Intelligent Robotic & Systems 83(2), 315–333 (2016). https://doi.org/10.1007/s10846-016-0333-4

    Article  Google Scholar 

  82. Wang, T., Chen, Y., Liang, J., Wang, C., Zhang, Y.: Combined of vector field and linear quadratic Gaussian for the path following of a small unmanned helicopter. IET Control Theory Appl. 6(17), 2696–2703 (2012). https://doi.org/10.1049/iet-cta.2012.0270

    Article  MathSciNet  Google Scholar 

  83. Wang, Y., Wang, X., Zhao, S., Shen, L.: Vector field based sliding mode control of curved path following for miniature unmanned aerial vehicles in winds. J. Syst. Sci. Complex. 31(1), 302–324 (2018). https://doi.org/10.1007/s11424-018-8006-y

    Article  MathSciNet  MATH  Google Scholar 

  84. Yamasaki, T., Balakrishnan, S.N., Takano, H.: Integrated guidance and autopilot for a path-following UAV via high-order sliding modes. In: 2012 American Control Conference (ACC), Proceedings of the American Control Conference, pp. 143–148 (2012)

  85. Zhao, B., Xian, B., Zhang, Y., Zhang, X.: Nonlinear robust sliding mode control of a quadrotor unmanned aerial vehicle based on immersion and invariance method. Int. J. Robust Nonlinear Control 25(18), 3714–3731 (2015). https://doi.org/10.1002/rnc.3290

    Article  MathSciNet  MATH  Google Scholar 

  86. Zhaowei, M., Tianjiang, H., Lincheng, S., Weiwei, K., Boxin, Z., Kaidi, Y.: An iterative learning controller for quadrotor UAV path following at a constant altitude. In: 2015 34th Chinese Control Conference (CCC), pp. 4406–4411. https://doi.org/10.1109/ChiCC.2015.7260322 (2015)

  87. Zhou, B., Satyavada, H., Baldi, S.: Adaptive path following for unmanned aerial vehicles in time-varying unknown wind environments. In: 2017 American Control Conference (ACC), pp. 1127–1132. https://doi.org/10.23919/ACC.2017.7963104 (2017)

  88. Zhou, D., Schwager, M.: Vector field following for quadrotors using differential flatness. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 6567–6572 (2014)

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This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SCAV (ref. MINECO DPI2017-88403-R). Bartomeu Rubí is also supported by the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya, the European Social Fund (ESF) and the AGAUR under a FI grant (ref. 2017FI B 00212).

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Rubí, B., Pérez, R. & Morcego, B. A Survey of Path Following Control Strategies for UAVs Focused on Quadrotors. J Intell Robot Syst 98, 241–265 (2020). https://doi.org/10.1007/s10846-019-01085-z

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