Interval Type-2 Fuzzy Cognitive Map-Based Flight Control System for Quadcopters
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Interval Type-2 Fuzzy Cognitive Map-Based Flight Control System for Quadcopters Abdollah Amirkhani1
•
Masoud Shirzadeh2 • Tufan Kumbasar3
Received: 6 February 2020 / Revised: 5 June 2020 / Accepted: 10 August 2020 Ó Taiwan Fuzzy Systems Association 2020
Abstract In this paper, we propose a novel Interval Type2 (IT2) Fuzzy Cognitive Map (FCM)-based flight control system to solve the altitude, attitude and position control problems of quadcopters. The proposed IT2-FCM encompasses all concepts related to drone for a satisfactory pathtracking and stabilizing control performance. The degree of mutual influences of the concepts is designed with opinions of three experts that take account the dynamics of drone and rules governing proportional integral derivative (PID) controllers. To model the inter-uncertainty of the experts’ opinions, IT2 fuzzy logic systems are utilized as they are powerful tools to model high level of uncertainties. Thus, the proposed IT2-FCM has a qualitative representation as it merges the advantages of IT2 fuzzy logic systems and FCMs. We present comparative simulations results in presence of uncertainties where the superiority of the proposed IT2-FCM-based flight control system is shown in comparison with its type-1 fuzzy counterpart.
& Abdollah Amirkhani [email protected] 1
School of Automotive Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran
2
Department of Electrical Engineering, Amirkabir University of Technology, Tehran 15875-4413, Iran
3
Department of Control and Automation Engineering, Faculty of Electrical and Electronic Engineering, Istanbul Technical University, Istanbul, Turkey
Keywords Interval type-2 fuzzy sets Quadcopters Fuzzy Cognitive Map Flight control system
1 Introduction In the last two decades, quadcopters were used in various missions such as search and rescue, surveillance, traffic monitoring, etc. [1–3]. This is due to their ability of vertical takeoff and landing, hovering, and sustaining a stable flight by using the torques generated by their motors [4]. The rotors of the quadrotor have a constant blade pitch; so, its flight is managed by varying the angular velocity of rotors. However, the control of quadcopters is cumbersome and hard to achieve as it inherits nonlinear coupled dynamics and has an under-actuated nature [5]. Moreover, the effects of unmodeled dynamics, aerodynamic disturbances, and uncertainties cannot be overlooked in the modeling of quadrotors, and this naturally creates a big challenge in the design of the flight control system. In literature, different methods have been presented for controlling a quadrotor, including the proportional integral derivative (PID) control [6], fuzzy logic [7, 8], feedback linearization [9], slidingmode [10], back-stepping [11] and robust [12]. The flight control problem of quadcopters consists of stabilizing the attitude and altitude dynamics and trajectory tracking in the Cartesian space [5]. The attitude and altitude control problem handles the orientation of quadrot
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