Fuzzy Logic for Speed Control in Object Tracking Inside a Restricted Area Using a Drone

  • Richard Navas Jácome
  • Harley Lovato Huertas
  • Patricia Constante ProcelEmail author
  • Andrés Gordón Garcés
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 152)


This article presents the autonomous speed and positioning control for a drone using fuzzy logic to track a target within a restricted area. Classical controllers present a problem, as in general they only support one input and one output and a system model is always required. For this application, it is necessary to analyze two inputs, the position in “x” and the position in “y” of an object that will be recognized by the drone through artificial vision. The goal is to control the speed at which the drone moves according to the position of the object detected by the machine vision within a restricted area, resulting in a faster or slower movement that will improve the tracking of a moving target by delivering real-time object monitoring information to the user in order to take some action based on this information.


Fuzzy control Drone Artificial intelligence 



Thanks to the University of the Armed Forces ESPE for the support provided to this research work.


  1. 1.
    Ramírez, N.V., Laguna Estrada, M.: La lógica borrosa: conjuntos borrosos, razonamiento aproximado y control borroso (2012).–55-65.pdf. Accessed 20 November 2017
  2. 2.
    Camastra, F., Ciaramella, A., Giovannelli, V., Lener, M.: A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference, ScienceDirect, 1710–1716 (2015)Google Scholar
  3. 3.
    Olivares Mendez, M., Mejias, L.: See-and-avoid quadcopter using fuzzy control. In: IEEE World Congress on Computational Intelligence, p. 1 (2012)Google Scholar
  4. 4.
    Gallacher, D.: Drone applications for environmental management in, Science Target, p. 1 (2016)Google Scholar
  5. 5.
    Scott, J.E., Scott, C.H.: Drone delivery models for healthcare. In: 50th Hawaii International Conference on System Sciences, p. 1 (2017)Google Scholar
  6. 6.
    Restas, A.: Drone applications for supporting disaster, SciRes, p. 1 (2015)Google Scholar
  7. 7.
    Saha, A.K., Saha, J., Ray, R.. Sircar, S.: IOT-based drone for improvement of crop quality. IEEE, p. 1 (2018)Google Scholar
  8. 8.
    Hausman, K., Müller, J., Hariharan, A., Ayanian, N., Sukhatme, G.S.: Cooperative control for target tracking with onboard sensing. Exp. Robot. 879–892 (2015)Google Scholar
  9. 9.
    Yang, W.-S., Chun, M.-H., Jang, G.-W.: A study on smart drone using quadcopter and object tracking techniques. IEEE, p. 1 (2018)Google Scholar
  10. 10.
    Gatica, N., Muñoz, C., Sellado, P.: Real fuzzy PID control of the UAV AR.Drone 2.0 for, IEEE (2017)Google Scholar
  11. 11.
    Olivares Mendez, M., Kannan, S., Voos, H.: Vision based fuzzy control autonomous landing with UAVs: From, IEEE, p. 1 (2015)Google Scholar
  12. 12.
    Indrawati, V., Prayitno, A., Utomo, G.: Comparison of two fuzzy logic controller schemes. IEEE (2015)Google Scholar
  13. 13.
    González Morcillo, C.: Lógica Difusa. técnicas de Softcomputing.
  14. 14.
    Santos, M., Suescun, E.M.: Aplicación de la lógica difusa en el ambito de las energías renovables (2012).
  15. 15.
    Ören, A., Koçyığıt, Y.: Landing sequencing modelling with fuzzy logic: opportunistic approach for unmanned aerial systems. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 943–948, (2016)Google Scholar
  16. 16.
    Lovato, A.V., Oliveira, J.C.M.: Airplane level changes using fuzzy control. In: International Conference on Fuzzy Systems (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Richard Navas Jácome
    • 1
  • Harley Lovato Huertas
    • 1
  • Patricia Constante Procel
    • 1
    Email author
  • Andrés Gordón Garcés
    • 1
  1. 1.Universidad de las Fuerzas Armadas ESPESangolquíEcuador

Personalised recommendations