Intelligent control apparently implies a set of subsystems in aerospace platforms which either blindly offer the possibility of sensor and effector functionality to associated meta-view agents of some form, or possibly have some intrinsic sensibility, in their own right.

Potentially learning using artificial intelligence mechanisms generating ongoing generated persistent state, representing tuned relationships amongst protocol elements, and eccentricities of abstractions supported, in the context of a private aerospace network, implies a teamwork approach of some form between sensors and effectors.

In effect, for example, the application of Bayesian methods with appropriate learning paradigms might allow self tuning and adaptation to some degree, of a collection of complex heterogeneous aerospace platforms and subsystems.

The practical scalability of such an approach, possibly similar to the situation with intelligent optical networks, implies careful attention to initial design considerations.