Linear and neural network feedback for flight control decoupling

Some experts are of the opinion that the task of flight training can become far less labor-intensive if the pilot can directly control each of the state variables of the aircraft individually. Yet complete decoupling of the aircraft as a nonlinear system is a formidable problem. Such a task requires accurate aircraft state information and rapid computing. The difficulties are compounded when the dynamics or the aerodynamics of the aircraft fall in the highly nonlinear regimes. The authors demonstrate the potential for an artificial neural network in conjunction with a linear compensator to perform such a function. The authors show that the linear compensator is unable to control the aircraft in the absence of the neural network. A neural network can be trained to produce the large nonlinear portion of the control inputs; however, a hybrid combination of the neural network and the compensator based on the linearized equations of motion gives the best results. Furthermore, The authors demonstrate that such a hybrid system can tolerate a large amount of noise in the network input. Several examples are shown, with and without the linear compensator. Finally, the authors demonstrate generalization within the training domain through accurately predicting a case that was absent in the training domain.