Visual-based quadrotor control by means of fuzzy cognitive maps. (January 2016)

• Record Type: Journal Article
• Authors: Amirkhani, Abdollah; Shirzadeh, Masoud; Papageorgiou, Elpiniki I.; Mosavi, Mohammad R.
• Is Part Of: ISA transactions. Volume 60(2016:Jan.)
• Page Start: 128
• Page End: 142
• Publication Date: 2016-01
• Subjects: Image-based visual servoing -- Moving target -- Fuzzy cognitive map -- Perspective image moments -- Nonlinear Hebbian learning; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2015.11.007
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: By applying an image-based visual servoing (IBVS) method, the intelligent image-based controlling of a quadrotor type unmanned aerial vehicle (UAV) tracking a moving target is studied in this paper. A fuzzy cognitive map (FCM) is a soft computing method which is classified as a fuzzy neural system and exploits the main aspects of fuzzy logic and neural network systems; so it seems to be a suitable choice for implementing a vision-based intelligent technique. An FCM has been employed in implementing an IBVS scheme on a quadrotor UAV, so that the UAV can track a moving target on the ground. For this purpose, by properly combining the perspective image moments, some features with the desired characteristics for controlling the translational and yaw motions of a UAV have been presented. In designing a vision-based control method for a UAV quadrotor, there are some challenges, including the target mobility and not knowing the height of UAV above the target. Also, no sensor has been installed on the moving object and the changes of its yaw angle are not available. Despite all the stated challenges, the proposed method, which uses an FCM in controlling the translational motion and the yaw rotation of a UAV, adequately enables the quadrotor to follow the moving target. The simulation results for different paths show the satisfactory performance of the designed controller. Highlights: Designing a visual-based controller by employing Fuzzy Cognitive Maps. Controlling the rotational and translational motion of a quadrotor by using FCMs. Training an FCM by the NHL algorithm for the purpose of designing a controller.