Skip to main content
SpringerLink
Log in

Optimal trajectory tracking control of unmanned aerial vehicle using ANFIS-IPSO system

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Accurate and precise trajectory tracking is crucial for unmanned aerial vehicle (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) controller and Improved Particle Swarm Optimization algorithm (IPSO) model based on functional inertia weight. The controller is implemented in a three degrees of freedom (3 DOF) quadrotor symbolized with its non-linear dynamical mathematical model. To achieve Cartesian position trajectory tracking capability, the construction of the controller can be divided into two stages: a regular ANFIS controller to guarantee fast convergence rapidity and IPSO aims to facilitate convergence to the ANFIS’s optimal parameters to accurately reproduce a desired reference trajectory. Simulation results are given to confirm the advantages of the proposed intelligent control, compared with ANFIS and PID control methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig.7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  1. Mostafa SA, Ahmad MS, Mustapha A (2017) Adjustable autonomy: a systematic literature review. Artif Intell Rev 1–38

  2. Lozano R (2010) Unmanned Aerial Vehicles: Embedded Control. Wiley, Hoboken

    Google Scholar 

  3. Barnhart R, Hottman S, Marshall D, Shappee E (2012) Introduction to unmanned aircraft systems. CRC Press, Taylor & Francis Group, Boca Raton, Fl

    Google Scholar 

  4. Nonami K, Kendoul F, Suzuki S, Wang W, Nakazawa D (2010) Autonomous flying robots: unmanned aerial vehicles and micro aerial vehicles. Springer, Berlin

    Book  Google Scholar 

  5. Austin R (2010) Unmanned Aircraft Systems: UAVS Design Development and Deployment. Wiley, New York

    Book  Google Scholar 

  6. Valavanis KP (2007) Advances in unmanned aerial vehicles: state of the art and the road to autonomy. Springer, Dordrecht

    Book  Google Scholar 

  7. De Filippis L, Guglieri G, Quagliotti F (2011) A minimum risk approach for path planning of UAVs. J Intell Robot Syst 61(1):203–219

    Article  Google Scholar 

  8. Primatesta S, Guglieri G, Rizzo A (2018) A risk-aware path planning method for unmanned aerial vehicles. In: International Conference on Unmanned Aircraft Systems (ICUAS), IEEE pp 905–913

  9. Mahadevan P (2010) The military utility of drones. CSS Anal Secur Policy 1–3

  10. Zhang L, Mu L, Liu Z, Zhang Y, Ai J (2018) Automated maneuvering decision for UAVs in forest surveillance and fire detection missions. International Conference on Unmanned Aircraft Systems (ICUAS), pp 1085–1090

  11. Ma L, Li MC, Wang YF, Tong LH, Cheng L (2013) Using high-resolution imagery acquired with an autonomous unmanned aerial vehicle for urban construction and planning. In: Proceeding International Confress on Remote Sensing, Environment and Transportation Engineering (RSETE 2013), Najing, China, 31, 200–203

  12. Knyaz VA, Chibunichev AG (2016) Photogrammetric techniques for road surface analysis. In: ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague, Czech Republic, 515–520. https://doi.org/10.5194/isprs-archives-XLI-B5-515-2016

    Article  Google Scholar 

  13. Kanistras K, Martins G, Rutherford MJ, Valavanis KP (2014) Survey of Unmanned Aerial Vehicles (UAVs) for traffic monitoring. In: Handbook of Unmanned Aerial Vehicles. Springer, pp 2643–2666. https://doi.org/10.1109/ICUAS.2013.6564694

  14. Chow JYJ (2016) Dynamic UAV-based traffic monitoring under uncertainty as a stochastic arc-inventory routing policy. Int J Transp Sci Technol 5(3):167–185

    Article  Google Scholar 

  15. Reinartz P, Lachaise M, Schmeer E, Krauss T, Runge H (2006) Traffic monitoring with serial images from airborne cameras. ISPRS J Photogramm Remote Sens 61:149–158. https://doi.org/10.1016/j.isprsjprs.2006.09.009

    Article  Google Scholar 

  16. European Aviation Safety Agency (2016) Prototype. Commission Regulation on Unmanned Aircraft Operations, Cologne, Germany

    Google Scholar 

  17. Siebert S, Teizer J (2014) Mobile 3D mapping for surveying earthwork projects using an unmanned aerial vehicle (UAV) system. Autom Constr 41:1–14. https://doi.org/10.1016/j.autcon.2014.01.004

    Article  Google Scholar 

  18. Valente J, Del Cerro D, Barrientos A, Sanz D (2013) Aerial coverage optimization in precision agriculture management: a musical harmony inspired approach. Comput Electron Agric 99:153–159

    Article  Google Scholar 

  19. Freeman PK, Freeland RS (2014) Politics and technology: US polices restricting unmanned aerial systems in agriculture. Food Policy 49(1):302–311

    Article  Google Scholar 

  20. Wang R, Liu J (2018) Trajectory tracking control of a 6-DOF quadrotor UAV with input saturation via backstepping. J Frankl Inst Eng Appl Math 355(7):3288–3309

    Article  MathSciNet  Google Scholar 

  21. Chen F, Jiang R, Zhang K, Jiang B, Tao G (2016) Robust Backstepping sliding-mode control and observer-based fault estimation for a quadrotor UAV. IEEE Trans Ind Electron 63(8):5044–5056

    Article  Google Scholar 

  22. Liu H, Li D, Zuo Z, Zhong Y (2016) Robust three-loop trajectory tracking control for quadrotors with multiple uncertainties. IEEE Trans Ind Electron 63(4):2263–2274

    Google Scholar 

  23. Kamel M, Burri M, Siegwart R (2017) Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles. IFAC-PapersOnLine, 50(1), 3463–3469, ISSN 2405–8963, https://dx.doi.org/10.1016/j.ifacol.2017.08.849

    Article  Google Scholar 

  24. Ríos H, Falcón R, González OA, Dzul A (2019) Continuous sliding-mode control strategies for quadrotor robust tracking: real-time application. IEEE Trans Ind Electron 66(2):1264–1272

    Article  Google Scholar 

  25. Tian B, Lu H, Zuo Z, Zong Q, Zhang Y (2018) Multivariable finite-time output feedback trajectory tracking control of quadrotor helicopters. Int J Robust Nonlinear Control 28(1):281–295

    Article  MathSciNet  Google Scholar 

  26. Wendong G, Jie L, Chengzhi Q, Jing Z (2018) Trajectory tracking control for a quadrotor UAV via extended state observer. Syst Sci Control Eng 6(3):126–135. https://doi.org/10.1080/21642583.2018.1539931

    Article  Google Scholar 

  27. Jang JSR (1993) ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  28. Abido MA, Khalid MS, Worku MY (2015) An efficient ANFIS based PI controller for maximum power point tracking of PV systems. Arab J Sci Eng 40(9):2641–2651

    Article  Google Scholar 

  29. Ponce P, Molina A, Cayetano I, Gallardo J, Salcedo H, Rodriguez J (2015) Experimental fuzzy logic controller type 2 for a quadrotor optimized by anfis. IFAC PapersOnLine 48(3):2435–2441

    Article  Google Scholar 

  30. Barman B, Kanjilal R, Mukhopadhyay A (2016) Neuro-Fuzzy Controller Design to Navigate Unmanned Vehicle with Construction of Traffic Rules to Avoid Obstacles. Int J Uncertain Fuzziness Knowl Based Syst 24(3):433–449

    Article  Google Scholar 

  31. Ponce P, Molina A, Cayetano I, Gallardo J, Salcedo H, Rodriguez J, Carrera I (2016) Fuzzy logic sugeno controller type-2 for quadrotors based on anfis. Nature-inspired computing for control systems, Springer, Cham pp 195–230.

    Google Scholar 

  32. Solaiappan BS, Nagappan K (2017) AGC for multisource deregulated power system using ANFIS controller. Int Trans Electr Energ Syst 27(3):e2270. https://doi.org/10.1002/etep.2270

    Article  Google Scholar 

  33. Ounnas D, Ramdani M, Chenikher S, Bouktir T (2017) An efficient maximum power point tracking controller for photovoltaic systems using Takagi-Sugeno fuzzy models. Arab J Sci Eng 42:4971–4982. https://doi.org/10.1007/s13369-017-2532-0

    Article  Google Scholar 

  34. Kharola A (2017) Design of a hybrid adaptive neuro fuzzy inference system (ANFIS) controller for position and angle control of inverted pendulum (IP) systems. Int J Fuzzy Syst Appl (IJFSA) 5(1):27–42

    Article  Google Scholar 

  35. Khatoon S, Nasiruddin I, Shahid M (2017) Design and simulation of a hybrid PD-ANFIS controller for attitude tracking control of a quadrotor UAV. Arab J Sci Eng 42(12):5211–5229

    Article  MathSciNet  Google Scholar 

  36. Dorzhigulov A, Bissengaliuly B, Spencer BF Jr, Kim J, James AP (2018) ANFIS based quadrotor drone altitude control implementation on Raspberry Pi platform. Analog Integr Circuits Signal Process 95(3):435–445

    Article  Google Scholar 

  37. Aldair AA, Obed AA, Halihal AF (2018) Design and implementation of ANFIS-reference model controller based MPPT using FPGA for photovoltaic system. Renew. Sustain. Energy Rev. 82(Part 3):2202–2217, https://doi.org/10.1016/j.rser.2017.08.071

    Article  Google Scholar 

  38. Ismail MM, Bendary AF (2020) Smart battery controller using ANFIS for three phase grid connected PV array system. Math Comput Simulation 167:104–118

    Article  MathSciNet  Google Scholar 

  39. Gaur P, Singh B, Mittal AP, Arora S (2019) Implementation of ANFIS Controller-Based Algorithm Measuring Speed to Eliminate RDC Hardware in Resolver-Based PMSM. In: Singh S, Wen F, Jain M (eds) Advances in System Optimization and Control Lecture Notes in Electrical Engineering. Springer, Singapore, pp 147–159

    Chapter  Google Scholar 

  40. Dixit TV, Yadav A, Gupta S, Abdelaziz AY (2019) Power Extraction from PV Module Using Hybrid ANFIS Controller. In: Precup RE, Kamal T, Zulqadar Hassan S (eds) Solar Photovoltaic Power Plants Power Systems. Springer, Singapore, pp 209–232

    Chapter  Google Scholar 

  41. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Perth, Australia, pp 1942–1948

  42. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, New York, pp 39–43

  43. Eberhart R, Simpson P, Dobbins R (1996) Computational Intelligence PC Tools. Academic Press Professional, Inc., San Diego 0-12-228630-8

    Google Scholar 

  44. Dréo J, Petrowski A, Siarry P, Taillard E (2003) Métaheuristiques pour l’optimisation Difficile. Eyrolles, Paris

    Google Scholar 

  45. Clerc M, Siarry P (2009). Une méthode inspirée de comportements coopératifs observés dans la nature : l’optimisation par essaim particulaire. Revue d’Electricité et d’Electronique REE, pp 25–32

  46. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: CEC 99. Proceedings of the congress on IEEE evolutionary computation, vol 3, pp 1945–1950.

Download references

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author information

Authors and Affiliations

  1. Département D’Informatique, Université Des Sciences Et de La Technologie D’Oran USTO’MB, 31000, Oran, Algérie

    Boumediene Selma & Samira Chouraqui

  2. Laboratoire de Génie Informatique Et D’Automatique de L’Artois (LGI2A), Univ. Artois, EA 3926, 62400, Béthune, France

    Hassane Abouaïssa

Authors
  1. Boumediene Selma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samira Chouraqui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hassane Abouaïssa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Boumediene Selma.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Selma, B., Chouraqui, S. & Abouaïssa, H. Optimal trajectory tracking control of unmanned aerial vehicle using ANFIS-IPSO system. Int. j. inf. tecnol. 12, 383–395 (2020). https://doi.org/10.1007/s41870-020-00436-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-020-00436-6

Keywords

Search

Navigation