Direct Adaptive Reconfigurable Control of a Tailless Fighter Aircraft

This paper describes a neural network based direct adaptive control approach to the problem of reconfigurable flight control. A Tailless fighter aircraft configuration with multiple and redundant control actuation devices is used to illustrate the level to which handling qualities can be maintained in the presence of large scale failures in the actuation channels. Of significance here is the speed with which recovery and maintenance of handling qualities can take place. The main advantage lies in eliminating the need for parameter identification during the recovery phase, and limiting the potential need for parameter identification to the problem of control allocation following the failure. A second by-product of this work is that the need for an accurate aerodynamic data base for purposes of flight control design can be significantly reduced. Moreover, the need for extensive off-line analysis, in-flight tuning and validation of gain schedules, and contingency coding necessary to handle a large set of possible failure modes is substantially reduced. Thus the overall approach may also be viewed as a direct path to substantially reducing the cost associated with the development of new aircraft.