Examples of simulation results are given in figures ( Figure 1& 2).Īs we can see from the time responses of the path angle and the altitude displayed for a RPV (Figure 1&2), the longitudinal motion of the considered airplane is unstable. 1), we can simulate the longitudinal responses of the considered airplane at a flight point, z 0 =2500 m and v 0 = 40 m/s. d m is taken as the control input of the airplane longitudinal motion.īy using the complete model (eq. ( v ˙ γ ˙ α ˙ q ˙ z ˙ ) = ( − D + T c o s α − m g s i n γ m L + T s i n α − m g c o s γ m v q − L + T s i n α − m g c o s γ m v M I y − v s i n γ ) v: airspeed, g : flight path angle, a: angle of attack, q pitch rate, z: altitude, D: drag force, L: lift force, M: pitching moment, I y: y-axis moment of inertia, m: aircraft mass, g: gravity acceleration and d m: elevator angle, T: thrust. The non linear equations of the longitudinal motion of a rigid aircraft can be written 6, 7 Altitude control is a longitudinal problem, and in this application, we will design an autopilot that controls the altitude of an RPV aircraft. However, under certain assumptions, they can be decoupled into the longitudinal and lateral equations. The equations governing the motion of an aircraft are a very complicated set of non-linear coupled differential equations. The effectiveness of the proposed approach is displayed by simulation results in the case of a longitudinal control. The design of a neural controller is given in section 4.

The third section outlines the principles of a linear controller. The paper is organized as follows: Section 2 presents the longitudinal dynamics of a rigid airplane. The application is focused on a remotely piloted vehicle (RPV). Emphasis is placed on the use of a command and stability augmentation system using an off-line trained network. 4,5 This paper presents the design of a flight controller using neural networks. In addition, a number of flight control applications illustrated the on-line learning capability of neural networks. Recently, neural networks have been proposed as feed-forward inverse dynamics controllers. However, relationships between physical variables are non linear and only represented by discrete numerical tables. 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.

1-3 They are generally applied to control task such as trajectory tracking and optimization. In the last years, several control theories have been widely developed.

Model based flight control system