This paper presents a
Aircraft carrier landings have been regarded as one of the most challenging phases of flight due to the extremely tight space available for touchdown on the flight deck of an aircraft carrier. The pilot must aim for a single spot on the flight deck with a very small margin for error [
While the topic of automated carrier landing has been studied for several decades, most researchers have focused on linear control methods. H-dot and glide-slope feedback are used as their bases which are found in Ref. [
In this paper, nonlinear dynamic inversion based controller is designed for carrier landing flight control system. Then
In this section, a nonlinear UAV flight dynamics model is developed. Then, the carrier landing environment is described.
The aircraft which was used for this study was the Joint Unmanned Combat Air System (J-UCAS) Equivalent Model (EQ model) developed by the Air Force Research Laboratory (AFRL) [
Summary of EQ model physical parameters.
Parameter | Value |
---|---|
Weight | |
Wing area | 75.12 m2 |
Mean aerodynamic chord | 5.68 m |
Wing span | 16.67 m |
Aspect ratio | 3.70 |
0.00 kg·m2 |
The simulation uses the standard equations of motion and kinematic relations found in a variety of standard references on flight dynamics.
The Nimitz-class carrier is employed in this paper. There are 4 wires spaced 40 feet apart for aircraft landing. The detailed parameters can be found in Ref. [
Summary of sea state perturbations.
Sea state | Perturbation | Amplitude | Frequency |
---|---|---|---|
4 | Roll | 0.6223 deg | 0.2856 rad/sec |
Pitch | 0.5162 deg | 0.5236 rad/sec | |
Surge | 0.9546 ft | 0.3307 rad/sec | |
Sway | 1.4142 ft | 0.3307 rad/sec | |
Heave | 2.2274 ft | 0.3491 rad/sec | |
5 | Roll | 0.9829 deg | 0.2856 rad/sec |
Pitch | 0.8202 deg | 0.5236 rad/sec | |
Surge | 1.5203 ft | 0.3307 rad/sec | |
Sway | 2.2627 ft | 0.3307 rad/sec | |
Heave | 3.5638 ft | 0.3491 rad/sec | |
6 | Roll | 1.4425 deg | 0.2856 rad/sec |
Pitch | 1.2374 deg | 0.5236 rad/sec | |
Surge | 2.2840 ft | 0.3307 rad/sec | |
Sway | 3.3941 ft | 0.3307 rad/sec | |
Heave | 5.3528 ft | 0.3491 rad/sec |
A large source of touchdown error is the turbulent air environment found in the approach path [
The wind turbulence model is given by
In this section, a
Flight control system block diagram.
Nonlinear dynamic inversion is a technique in which feedback is used to linearize the system to be controlled and to provide desired dynamic [
The inner-loop dynamics are presented below:
With the pseudocontrol signal
Inserting equation (
Equation (
The out-loop dynamics inversion controller is designed in much the same way as the interloop controller. The out-loop dynamics are presented below:
With the pseudocontrol signal
Inserting equation (
If the estimates in equation (
The NDI-based controller described assumes exact knowledge of the system. However, this is not the case. To address robustness to model uncertainties, a
The pitch channel under baseline controller can be written in the form of
The system (
The system above verifies the following assumptions [
There exists
For arbitrary
The
In this study, the uncertainties are mainly caused by aerodynamic parameters and the wind disturbance. They are always uniformly bounded and limited in how fast they can change. Thus, these assumptions are reasonable.
The structure of the
Block diagram of
A piecewise constant State predictor
Adaptive law
Control law
We define the closed-loop reference system as [
Given the adaptive closed-loop system with the
As the sampling period
the low-pass filter
The desired dynamics for pitch channel is set by a parameter
The low-pass filter guarantees that the control signal stays in the low-frequency range even in the presence of fast adaptation. In Ref. [
As presented before, the sampling time
In order to examine the performance of
Pitch angle capture simulation.
In this case, aerodynamic parameters are varied by 20%. Mass and mass inertia properties are varied by 5%. The control effectiveness are varied by 50%. Figure
UAV carrier landing simple simulation.
In this case, turbulence and sea state effects are not included. The forward speed of UAV is
UAV carrier landing Monte Carlo simulation.
In this case, the performance of the controller is evaluated through a Monte Carlo simulation. In order to compare with Ref [
Pitch angle capture task without parameter perturbations.
Pitch angle capture task with parameter perturbations.
UAV landing trajectory in the vertical plane.
UAV Landing trajectory in the horizontal plane.
Wind turbulence components.
Deck motion under sea state 4.
The number of 500 Monte Carlo simulation experiments are carried out. In this case, wind turbulence and sea state effects are included. The landing dispersions under NDI baseline controller with linear compensation can be found in Ref [
Landing dispersion under NDI controller with
In this paper, the design of a nonlinear inversion controller for UAV carrier landing has been developed. Furthermore, it has been explained how to augment this baseline controller with a
This publication is supported by multiple datasets, which are openly available at locations cited in References.
The authors declare that there is no conflict of interest regarding the publication of this paper.
This work was supported by the Aeronautical Science Foundation of China (Grant No. 20175752045; 2016ZA02001).