"Artificial Neural Network" capabilities
Tourist,
Reading quickly through the Dryden "Self-repairing Flight Control Systems" paper, if I understand it correctly an Artificial Neural Network (ANN) flight control system would use continuous feedback from the control surfaces so that under damage or failure conditions, the system re-allocates control to non-traditional flight control surfaces and/or incorporates propulsion control, when additional control power is necessary for achieving desired flight control performance, and that is now well proven.
In this discussion, the relevance would be that much of the human pilot creativity demonstrated in the United DC10 uncontained failure could have been replaced by automation. Also much of the aircraft flyability aspects of Qantas A380 uncontained failure. So I take your point about that aspect having been addressed - to a degree.
So can you explain how an ANN would deal with e.g. the BA 747 Nairobi T/O event, where prior human failures involving engine and airframe hardware, and system logic, resulted in undamaged surfaces ending up incorrectly configured for the actual flight regime?
In CX 780 where the engine fuel control units simply got "stuck" and unresponsive, the 70%/17% assymetry problem could also be handled automatically at least in some parts of the flight envelope by such ANN reconfiguring. And by chance the total energy being delivered was not too much to prevent the crew landing.
But if both had been at high power the outcome might have been different. From what I have read it appears that that for an ANN to be effective, there needs to be a transition to a lot more involvement of electrics in actuation as well. E.g in the Dryden document there's a picture of the F15 wing opened up for the replacement of mechanical actuators with electric servos. In other cases engines and other mechanical components may simply ignore control inputs including shut down. How does an ANN deal with that?