• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.422-428
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• 2007

This paper presents a neural network based adaptive control approach to a reconfigurable flight control law that keeps handling qualities in the presence of faults or failures to the control surfaces of an aircraft. This approach removes the need for system identification for control reallocation after a failure and the need for an accurate aerodynamic database for flight control design, thereby reducing the cost and time required to develope a reconfigurable flight controller. Neural networks address the problem caused by uncertainties in modeling an aircraft and pseudo control hedging deals with the nonlinearity in actuators and the reconfiguration of a flight controller. The effect of the reconfigurable flight control law is illustrated in results of a nonlinear simulation of an unmanned aerial vehicle Durumi-II.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.7
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• pp.35-44
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• 2006

When control surface failure occurs, it is conventional to correct a current control or to transform to other control. In this paper, instead of adopting a conventional way, a reconfiguration method which compensate the failure with alternative control surface deflection, depending on the level of failure, by using neural network and PCH(Pseudo-Control Hedging). The Conroller is designed of inner-loop(SCAS : Stability Command Augmentation System) with DMI(Dynamic Model Inversion) and outer-loop with Y axis acceleration feedback for a coordinate turn. Additionally, double PCH method was adopted to prevent actuator saturation and input command was generated to compensate for failure. At the end, The feasibility of the method is validated with randomly selected failure scenarios.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.315-320
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• 2003

This paper presents a control augmentation system (CAS) based on the dynamic model inversion (DMI) architecture for a highly maneuverable aircraft. In the application of DMI not treating actuator dynamics, significant instabilities arise due to limitations on the aircraft inputs, such as actuator time delay based on dynamics and actuator displacement limit. Actuator input saturation usually occurs during high angles of attack maneuvering in low dynamic pressure conditions. The pseudo-control hedging (PCH) algorithm is applied to prevent or delay the instability of the CAS due to a slow actuator or occurrence of actuator saturation. The performance of the proposed CAS with PCH architecture is demonstrated through a nonlinear flight simulation.

• 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.