Abstract
SUMMARY This paper presents a direct adaptive recongurable #ight control approach and demonstrates its e!ectiveness via an application to an advanced tailless ghter aircraft. The recongurable control law is based on a dynamic inversion controller in an explicit model following architecture. An on-line neural network is used to adaptively regulate the error between the desired response model and the actual vehicle response. An on-line control allocation scheme generates individual control e!ector commands to yield the moments commanded by the controller, while prioritizing critical axes and optimizing performance objectives such as maneuver load alleviation. An on-line system identication module generates estimates of the vehicle’s stability and control derivatives for use in control allocation and command limiting. The recongurable control laws are demonstrated by comparing their performance to a dynamic inversion control law when unknown failure/damage are induced. Copyright ( 1999 John Wiley & Sons, Ltd.
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