• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.2
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• pp.141-149
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• 2018

This paper presents a neural network controller design for complex damage to a blended wing Unmanned Aerial Vehicle(UAV): partial loss of main wing and vertical tail. Longitudinal/lateral axis instability and the change of flight dynamics is investigated via numerical simulation. Based on this, neural network based adaptive controller combined with two types of feedback linearization are designed in order to compensate for the complex damage. Performance of two kinds of dynamic inversion controllers is analyzed against complex damage. According to the structure of the dynamic inversion controller, the performance difference is confirmed in normal situation and under damaged situation. Numerical simulation verifies that the instability from the complex damage of the UAV can be stabilized via the proposed adaptive controller.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.5
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• pp.342-349
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• 2013

A damage imposed on an unmanned aerial vehicle changes the flight dynamic characteristics, and makes difficult for a conventional controller based on undamaged dynamics to stabilize the vehicle with damage. This paper presents a neural network based adaptive control method that guarantees stable control performance for an unmanned aerial vehicle even with damage on the main wing. Additionally, Pseudo Control Hedging (PCH) is combined to prevent control performance degradation by actuator characteristics. Asymmetric dynamic equations for an aircraft are chosen to describe motions of a vehicle with damage. Aerodynamic data from wind tunnel test for an undamaged model and a damaged model are used for numerical validation of the proposed control method. The numerical simulation has shown that the proposed control method has robust control performance in the presence of wing damage.