Abstract
When military and civil missions such as transportation increase, fault tolerant control of unmanned aerial vehicles will be an obligation. Although onboard sensors provide information about the status of a quadrotor, the camera is not included in the list. In this study, visual servo control of quadrotors as a popular method for motion control is addressed. we address a visual servo control system for quadrotors as a popular method for motion control. The feature motions in the image plane are analyzed to reveal the relation between the actuator faults and these motions. Four AI fault approximators, a neural network, an extreme learning machine, a linear support vector machine, and a long short-term memory are used to approximate actuator faults of a quadrotor while using feature inputs. The results are convincing and the approximation results are used by a fuzzy logic unit to provide gain-scheduling based fault tolerant control. The proposed system shows sufficient results as a visual servo system for fixed and moving feature targets while providing fault tolerance.
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