A Nonlinear Automatic Landing Control System for a UAV

Abstract

An automatic landing system for an unmanned aerial vehicle (UAV) is presented in the following paper. The nonlinear aircraft model with thrust, elevator, rudder and aileron deflections as control inputs is established using the appropriate aerodynamic data. The flight trajectory the airplane is expected to travel during landing is then defined. A nonlinear control law, using feedback linearization method, is designed to develop the automatic landing controller for the UAV aircraft. A linear state-feedback control law is also designed for means of comparison with the nonlinear controller. The elevator is employed for longitudinal control whereas the rudder and aileron aid in lateral control. Thrust is the control input for velocity control, which is held constant during landing. A nonlinear simulation, incorporating wind shear and ground effects, is run using MATLAB/Simulink to assess the controllers’ integrity. The auto-landing system designed in this paper is meant to increase the autonomy of the UAV to eventually reach a fully autonomous system. Simulation results show the importance of designing the controller considering such effects. Landing trajectory tracking performance by the nonlinear controller is of great tone.

Dynamic Smooth Sliding Control Applied to UAV Trajectory Tracking

“…As in the original SSC, sliding mode occurs at σ ≡ 0 so that σ converges to zero in some finite time t s ∈ [0, t M ), FIGURE 2: General DSSC block diagram for arbitrary relative degree case and with generic state-dependent functions τ m (σ(t), e(t), t), k o (σ(t), e(t), t) and τ av (σ(t), e(t), t). The predictor is given in (18) and depends on k o and τ m , while the averaging filter is given in (15) and depends on τ av . For the class relative degree one plants considered here with ẏ available for feedback, one can set τ f = 0, so that σ f = σ with σ in (10).…”

“…This is the author's version which has not been fully edited and content may change prior to final publication. The DSSC algorithm is composed by: the tracking error in (9), the relative degree one variable σ in (10), predictor in (18) with the discontinuous term in ( 16), modulation function in (33), sliding variable σ in (17), DSSC law in (14), complete control in (11) and smooth averaging filter in (15).…”

“…respectively, where we have replaced u by the DSSC's control law u = −u av 0 , with u av 0 in (15). Now, one can subsequently conclude that: (i) the disturbance term d1 , in (20), reduces to d1 = (−l 0 ẏ + σm + k n p d); (ii) d2 , in (20), reduces to d2 = (σ/τ m + l 0 ẏ − σm ); (iii) d1 + d2 = k n p d + σ/τ m ; (iv) and d 0 , in (20), reduces to…”

“…Classic techniques such as the Proportional-Integral-Derivative (PID) control have been applied to most existing flight control systems, because of their simple design and implementation, when is reasonable to approximate the vehicle dynamics to a linear model [7]- [9], with uniformly norm-bounded or almost constant disturbances. When the linearization process [10] is not valid, non-linear control methods come into play, such as optimization strategies for path planning [11], linear quadratic regulation control (LQR) [12], [13], feedback linearization control schemes [14], [15] and robust strategies via sliding modes [16], [17]. In addition, the performance of classic techniques is, in general, limited by the presence of uncertainties, disturbances, and unmodelled dynamics.…”