Development of a reconfigurable flight control law for tailless aircraft

A neural-network-based direct adaptive control approach to the problem of reconfigurable flight control is described. The control law was first tested using a model of a tailless fighter aircraft configured with multiple and redundant control actuation devices and subsequently tested both in piloted simulation and in a flight test on the X-36 aircraft. The model aircraft was used for design and to illustrate the level to which handling qualities can be maintained in the presence of failures in the actuation channels. Of significance 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 in the problem of control reallocation following a failure. A second by-product is that the need for an accurate aerodynamic database for the purpose of flight control design can be significantly reduced. Moreover, the need for extensive offline 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.