On-line learning neural-network controllers for autopilot systems

This paper proposes the implementation of on-line learning neural controllers in the autopilot control laws of a modern high-performance aircraft. A first advantage of this design philosophy consists in avoiding the precomputation, storing, and interpolation between thousands of feedback gains of a typical flight control system. Another advantage is the ability to compensate for nonlinearities and model uncertainties. In addition, an on-line learning time-varying neural architecture will avoid the time-consuming gain recalculation following any modification to the aircraft or to its control system during its operative life. The implementation of these types of alternative controllers is made possible by the recent simultaneous advances in neural-network technology with regard to the availability of efficient and fast learning algorithms, along with progress in digital microprocessors. The approach is shown using a six-degree-of-freedom nonlinear simulation code. The traditional gain-scheduling-based control laws for typical autopilot functions are replaced by on-line learning neural architectures trained with the extended back-propagation algorithm. This algorithm has shown substantial improvements over the conventional back-propagation method in learning speed and accuracy. The results of the simulation with and without a man in the loop are presented and discussed.