Design of an Executable ANFIS-based Control System to Improve the Attitude and Altitude Performances of a Quadcopter Drone


Nowadays, quadcopters are presented in many life applications which require the performance of automatic takeoff, trajectory tracking, and automatic landing. Thus, researchers are aiming to enhance the performance of these vehicles through low-cost sensing solutions and the design of executable and robust control techniques. Due to high nonlinearities, strong couplings and under-actuation, the control design process of a quadcopter is a rather challenging task. Therefore, the main objective of this work is demonstrated through two main aspects. The first is the design of an adaptive neuro-fuzzy inference system (ANFIS) controller to develop the attitude and altitude of a quadcopter. The second is to create a systematic framework for implementing flight controllers in embedded systems. A suitable model of the quadcopter is also developed by taking into account aerodynamics effects. To show the effectiveness of the ANFIS approach, the performance of a well-trained ANFIS controller is compared to a classical proportional-derivative (PD) controller and a properly tuned fuzzy logic controller. The controllers are compared and tested under several different flight conditions including the capability to reject external disturbances. In the first stage, performance evaluation takes place in a nonlinear simulation environment. Then, the ANFIS-based controllers alongside attitude and position estimators, and precision landing algorithms are implemented for executions in a real-time autopilot. In precision landing systems, an IR-camera is used to detect an IR-beacon on the ground for precise positioning. Several flight tests of a quadcopter are conducted for results validation. Both simulations and experiments demonstrated superior results for quadcopter stability in different flight scenarios.

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The authors acknowledge the Hashemite University for providing the financial and technical supports for this project. Also the authors thank all colleagues and students at Jordan University and at the Hashemite University for their valuable assistance.

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Corresponding author

Correspondence to Mohammad Hayajneh.

Additional information

Mohammad Al-Fetyani received the B. Sc. degree in mechatronics engineering with an excellent grade at The University of Jordan, Jordan in 2020. He is currently working as a research assistant at The University of Jordan and intends to complete his postgraduate studies.

His research interests include robotics and intelligent systems.

Mohammad Hayajneh received the B. Sc. and M. Sc. degrees in mechatronics engineering from Jordan University of Science and Technology, Jordan in 2010 and 2012. He received the Ph. D. degree in automatic control and operational research from University of Bologna, Italy in 2016. He is currently an assistant professor in mechatronics engineering at The Hashemite University, Jordan.

His research interests include the design and development of control and navigation methods in aerial and ground robots and their applications.

Adham Alsharkawi received the B. Sc. degree in machatronics engineering from Tafila Technical University, Jordan in 2010. He received the M. Sc. degree in advanced control and system engineering from University of Manchester, UK in 2013. He received the Ph. D. degree in automatic control and systems engineering from University of Sheffield, UK in 2017. He is currently an assistant professor in Mechatronics Engineering Department, The University of Jordan, Jordan. He has been working in projects related to wheeled mobile robots, quadcopters, and solar thermal power plants.

His interests include system dynamics, automatic control, artificial intelligence, and solar energy.

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Al-Fetyani, M., Hayajneh, M. & Alsharkawi, A. Design of an Executable ANFIS-based Control System to Improve the Attitude and Altitude Performances of a Quadcopter Drone. Int. J. Autom. Comput. 18, 124–140 (2021).

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  • Quadcopter
  • proportional integral derivate (PID) control
  • fuzzy control
  • adaptive neuro-fuzzy
  • altitude control
  • attitude control