Flight control system design using neural networks

In the last years, several control theories have been widely developed.1–3 They are generally applied to control task such as trajectory tracking and optimization. In most cases, the control approaches are based on linear methods and on the assumption that precise analytical model of the controlled system is available. However, relationships between physical variables are non linear and only represented by discrete numerical tables. Recently, neural networks have been proposed as feed-forward inverse dynamics controllers. In addition, a number of flight control applications illustrated the online learning capability of neural networks.4,5 This paper presents the design of a flight controller using neural networks. Emphasis is placed on the use of a command and stability augmentation system using an off-line trained network. The application is focused on a remotely piloted vehicle (RPV). The paper is organized as follows: Section 2 presents the longitudinal dynamics of a rigid airplane. The third section outlines the principles of a linear controller. The design of a neural controller is given in section 4. The effectiveness of the proposed approach is displayed by simulation results in the case of a longitudinal control.