Application of Neural Network on Flight Control

Abstract

Over the last three decades, adaptive control has evolved as a powerful methodology for designing feedback controller of nonlinear systems. Most of the studies assume that the system nonlinearities are known a prior, which is generally not applicable in the real world. To overcome this drawback, from twenty years ago, there has been a tremendous amount of activity in applying Neural Networks for adaptive control. With their powerful ability to approximate nonlinear functions, neuro-controllers can implement the expected objectives by canceling or learning the unknown nonlinearities of the system to be cancelled. Neural Networks are specially suitable for the adaptive flight control applications where system dynamics are dominated by the unknown nonlinearities. In the past decades, major advances have been made in adaptive control of linear time-invariant plants with unknown parameters. The choice of the controller structure is based on well established results in linear systems theory and stable adaptive laws which assure the global stability of the overall system are derived based on properties of those system. In recent years, Artificial Neural Network based control strategies have attracted much attention because of their powerful ability to approximate continuous nonlinear functions .(1) In fact, a neural controller with on-line learning can adapt to the change in a system dynamics and hence is an ideal choice for controlling highly nonlinear system with uncertainty. For adaptive control purposes neural networks are used as approximation models of unknown nonlinearities. The input / output response of neural network models is modified by adjusting the values of its adjusting the values of its parameters. Although it is true that polynomials, trigonometric series and orthogonal functions can also be used as function approximator, neural networks have been found to be particularly useful for controlling highly uncertain, nonlinear and complex systems. Neural control strategies can be broadly classified into off-line and on-line schemes based on how the parameters of the network are tuned. When the neural controller operates in an on-line mode, it has no a priori knowledge of the system to be controlled and the parameters of the network are updated while the input - output data is received. In the off-line control, the network's parameters are determined from the unknown training pairs and then those parameters are fixed for control purposes. (2)