G. Tan et al. (Eds.): AsiaSim 2013, CCIS 402, pp. 528540, 2013. Springer-Verlag Berlin Heidelberg 2013

Lateral Carrier Landing Performance Affecting Factors of Small Carrier-Based UAV

Fengying Zheng1, Huajun Gong2, Ju Jiang2, and Ziyang Zhen2 1 Academy of Frontier Science, Nanjing University of Aeronautics & Astronautics,

Nanjing, 210016, China zhfy@nuaa.edu.cn

2 College of Automation Engineering, Nanjing University of Aeronautics & Astronautics,

Nanjing, 210016, China {ghj301,jiangju,zhenziyang}@nuaa.edu.cn

Abstract. Research on factors affecting carrier landing performance of small UAV has vital significance to ensure the safe landing of UAV. The influences of external environmental disturbance and UAV lateral attributes change are studied. First, Small UAV lateral carrier landing system is constructed. Comprehensive computer models, which include ship dynamics, airwake, navigational error, and airplane dynamics and kinematics, dynamic airplane control structure based on improved LQR with frontal compensator, are then designed to simulate unmanned carrier landings. Second, aircraft lateral attributes are varied and landing performance statistics are recorded for each configuration. Finally, the aircraft preliminary design limits are generated. Simulation results show that severe sea condition has the greatest impact on landing dispersion, and in lateral aerodynamic attributes, side force coefficient with sideslip angle which is generally negative is the most important factor. Increasing its absolute value with physical constraints will significantly improve landing performance.

Keywords: carrier UAV, flight control, landing performance, disturbance sources, lateral aerodynamic attributes.

1 Introduction

Unmanned systems are becoming increasingly important to our military, recent naval wars show that unmanned air vehicle (UAV) has a vital role in the future of the navy [1]. Due to carrier-based UAV is just getting started [2], less of the research results publicly report in the UAV carrier landing.

The Automatic carrier landing system has been in used for years [3], but it is neither flight critical nor attempts at severe sea-state because the pilot provide for reversion to manual control [4,5]. Going to a UAV raises the automated system to the status of flight critical and permits greater design freedom, no longer constrained by the limitation of the human operator. This research is intended to begin to know the design constraints imposed by fully autonomous carrier landing.

Miniaturization is the development trend of the carrier-based UAV [6], however, because of its small size, light weight, when landing, the small UAV is more

Lateral Carrier Landing Performance Affecting Factors of Small Carrier-based UAV 529

vulnerable to the impact of its aerodynamic attributes and outside disturbances. We must take account of these unfavorable aspects when we make research. Studies on carrier landing performance affecting factors are mainly concentrated on large or medium-sized UAV. Ref [7] uses dynamic inversion technology to analysis landing performance of an unmanned combat aerial vehicle. Ref [8] lists a variety of carrier aircraft landing specification. Ref [9] gives a medium-sized UAV design specification, especially focus on longitudinal performance. All of above references do not put much attention on the lateral system.

Aircraft Lateral dynamics is a more complex problem because of the coupling of the airplane's lateral and directional axes, plus the coupling of the ship's lateral and directional axes[9]. In this paper, we take an active small carrier UAV as model, develop its lateral dynamics and kinematics model, complete lateral flight control design, and analysis lateral landing performance include environment disturbance sources and attributes change. Landing performance statistics are recorded for each configuration. The influence of each attribute is extracted from the landing statistics. Finally, the aircraft preliminary design limits are generated. The controller design and simulation are conducted in the MATLAB/Simulink environment.

2 Small UAV Lateral Automatic Carrier Landing System

Main lateral landing performance of carrier aircraft includes mean and standard deviation of lateral position error, lateral deviation range, lateral boarding rate, etc, as shown in Table 1[10-11].

Table 1. Lateral touchdown dispersion index

Performance index Target Performance Allowable performance

Lateral Mean 0.61m 1.22 m Lateral Std Deviation 0.91m 1.52 m

Lateral Deviation range -1.52~1.52m -3.05~3.05m Boarding rate 75% 65%

For assessment of small UAV carrier landing performance under different conditions, the UAV lateral landing simulation system is constructed, as indicated in Figure 1.

e

T

Fig. 1. Structural allocation of small carrier UAV lateral landing system

530 F. Zheng et al.

Flight control system uses LQR controller with frontal compensator. The ship motion model and navigation error model are provided by the Naval Air Systems Command [10,11]. Airwake model references MIL-F-8785C military specification given specific atmospheric turbulence mathematical model for landing [12]. A ten-millisecond time step (0.01 sec) is selected to match models.

3 Lateral Control System

3.1 Small Carrier-Based UAV Modelling

This paper refers to small carrier-based unmanned aerial vehicle aerodynamic parameters [5]. First, establish the full nonlinear dynamics and kinematics model of UAV, then complete trim and linearization when given steady straight-line flight status as a reference point. finally get the linearized small perturbation equations. UAV state vectors are as follows:

[ ]g gx V p q r x y h = (1) Where V is flight speed, is angle of attack, is sideslip angle. , ,p q r are airplane angular velocity components about body-fixed axes. , , are airplane pitch angle, bank angle, and heading angle. , ,g gx y h are the components of airplane centre of mass along earth-fixed axes. According to the force equations in airflow axes, moment and kinematic equations in the body-fixed axes, we can establish the 12-order nonlinear coupled differential equations.

1 2 3 42 2

5 6 7

8 2 4 9

( cos cos ) /( sin ( cos sin cos sin sin )) / cos( cos sin ( sin cos )) /( )

( )( )

( cos sin ) t

xa

za

ya

V T D G mT L G mV p q r mVT Y G mV p r mV

p c r c p q c L c Nq c pr c p r c Mr c p c r q c L c N

p r q

= +

= + + +

= + + +

= + + +

= +

= + +

= + +

an

cos sin( cos sin ) / cos

cos cos (sin sin cos cos sin ) (sin sin cos sin cos )cos sin (sin sin sin cos cos ) ( sin cos cos sin sin )

sin sin cos cos cos

g

g

q rr q

x u v w

y u v w

h u v w

=

= +

= + + +

= + + + +

=

(2)

Subsequently, calculate trimmed variables when given benchmark height 0 100 mh = , benchmark velocity 0 30m / sV = , and selected performance index:

2 2 2J V q= + + (3)

Lateral Carrier Landing Performance Affecting Factors of Small Carrier-based UAV 531

The variables needed to trim are the angle of attack , elevator deflection e , throttle deflection T . Give the initial values of variables, use optimization algorithm, let J infinite close to zero, and obtain the corresponding optimal trimmed variables, as shown in figure 2, 0 2.4219 =

00.573e = 0 1.869T N = .

In accordance with the principle of small perturbations, Lateral aircraft dynamics can be characterized by a system of four linear differential equations, in a state-space formulation as follows.

0.238 0.0422 0.999 0.4666 0 0.11678.985 27.83 6.794 0 350.02 8.45322.504 0.946 1.020 0 11.85 15.10

0 1 0.0423 0 0 0

a

r

pprr

= +

(4)

Additionally, the kinematic equations are available:

0

0 0

0 0

/ cos( sin ) / cos

cos

r

y U

= = + =

(5)

The eigenvalues can be determined by finding eigenvalues of the matrix A:

0sI A = (6) The solution of the characteristic equation yields the eigenvalues:

1 2,3 427.6250; 0.7577 5.1402 ; 0.0452s s i s= = = (7) Correspond to the aircraft's roll mode, Dutch roll mode and spiral mode .The damping ratio and undamped natural frequency for Dutch roll mode are as follows:

0.1468, 5.196Dr Dr = = (8) Figure 3 shows a cross-lateral open-loop aircraft response when input the initial roll angle.

0 100 200-1

0

1

2

3

4

5

Iterations0 100 200

10-8

10-6

10-4

10-2

100

102

Iterations

J

e()

t(N)

()

0 2 4 6 8 10-0.5

0

0.5

1

1.5

2

p (/s)

r (/s)

()

()

t(s)

Fig. 2. trimmed variables and performance index Fig. 3. Aircraft Response to unit roll angle

532 F. Zheng et al.

From the response curve, we can see that sideslip angle and roll angle rate gradually stabilize, aond the roll angle and yaw angle rate gradually increases, the aircraft cannot be restored to original flying condition

3.2 Improved LQR Flight Controller Design Consider small UAVs vulnerability and poor flight stability, the LQR controller with strong robustness is used to design lateral flight control system. In order to minimize the observed tracking error without excessive control activity, we add frontal compensator to offset the closed-loop poles.

In this case the outputs of interest are lateral velocity perturbation and lateral deviation. The two values we selected to track are v and y . The controller is required to simultaneously maintain the desired lateral velocity and line-up as precisely as possible. To accomplish this, integral tracking on v and y is introduced to achieve zero steady-state error in response to a pure ramp input (yielded by the angle between the flight deck axis and ship axis, when ship motion). As a result, the establishment of the UAV augmented state equation as follow:

cX X u= + c cA B

(9)

Where: [ ]Tcy vX v p r ys s

=

0 0 0 0

(4,4)1 0 0 sin cos 0 0 00 0 0 0 0 1 0 01 0 0 0 0 0 0 0

zeros

u u

=

c

A

A

(4,2)zeros

= cB

B

The functional form of the performance index can be provided as a quadratic index:

0( )T TcJ y y u u dt

= + Q R (10) The matrix Q weights the output states, and R weights the control inputs. The output y is composed of the elements given in equation (11) below.

0 0 0 0

2( 2 ) 1 0 0 sin cos 2 2 00.5 1 0 0 0 0 0 0 0.5(1 )

c

s hu usy X

us

+ +

= = +

(11)

The effect is to place two zeros in the open-loop transfer function, which attract the closed-loop poles. Zero locations are determined by trial, attempting to minimize the observed tracking error without excessive control activity. Introduce optimal control law, provided as follow:

ec

Tu X

= =

K

(12)

Lateral Carrier Landing Performance Affecting Factors of Small Carrier-based UAV 533

Where K is constant state feedback gain matrix determined by solving the steady-state matrix Riccati equation:

, T -1 -1PA + A P - PBR BP + Q = 0 K = R BP

(13) In order to better control, it is needed to adjust the weighting matrix Q and R . And the choice of Q and R are compromised between adjustment speed and control capability of state, the larger Q can get a faster adjustment speed, and the lager R can make the necessary control capability reduced. For our problem, the weight values of Q and R are determined using the method suggested by Nelson[13]. Finally, have:

0.247 0.009 0.721 1.03 14.03 0.507 0.352 0.0850.53 0.0312 0.245 1.12 8.234 0.29 0.183 0.177

=

K

(14)

However, Note that the implementation system and the system for gain calculations are not the same. The difference is the gains are calculated for y and /y s making it a regulator problem, but the implementation used errory and /errory s transforming it to a tracking problem. Provided as control law:

', ' [ ]e Tcmdc c cmdT

y y vu X X v p r y y

s s

= = = K (15)

Where cmdy is line-up command added deck motion.

Figure 4 shows the response diagram of the flight control system while input unit step lateral deviation signal.Figure5 shows the response while input unit ramp signal superimposed amplitude 1m, frequency 0.5 rad / s sinusoidal signal.

It can be seen from the figure, the flight control system designed can quickly and accurately track command. Tracking unit step signal, steady-state error is zero, and control surfaces restore fast, tracking slope superimposed sinusoidal signal, steady state error does not exceed 0.5m. So the improved LQR controller designed can be a general method for small UAV flight control law design.

0 20 40-0.2

-0.1

0

t(s)

v (m

/s)

0 20 40-10

-5

0

5

t(s)

p (

/s)

0 20 40-2

0

2

4

t(s)

r (

/s)

0 20 40-1

0

1

2

t(s)

y(m

)

0 20 40-0.5

0

0.5

t(s)

a(

)

0 20 40-0.5

0

0.5

1

t(s)

r(

)

Fig. 3. Flight control system response to unit step command

534 F. Zheng et al.

0 10 20 300

10

20

30

40

50

60

t(s)

0 10 20 30-1

-0.5

0

0.5

1

t(s)

trac

king

erro

r(m)

commandactual

Fig. 4. Flight control system response to given command

4 Performance Analysis with Disturbance Sources

4.1 Ship Dynamic

The dynamics and dimensions of CVN 65, the U.S.S. Enterprise, are employed throughout the research. The distances from the ships center of motion to the landing area in feet are 68m aft, 19.5m up, and 3m left. Carrier geometry is shown as figure 6. The ship dynamics model provided generate six degree-of-freedom time histories of ship motion for a sea-state chosen by the user. Note that all ship displacements are referenced to the ship center of motion. Since the desired touchdown point(DTP) is displaced a lateral distance from the center of motion, the DTP's translational displacement is dependent upon both the translational and angular displacements of the ship. These angles had to be converted to distances Y to be useful to the landing task. Equations (16) given below are the exact lateral relationships.

19.5sin( ) 3cos( ) cos( ) 3 68sin( )DTP s s s s sY y = + + + (16) Where, , ,s s s are pitch, roll and yaw angle of ship, sy is lateral translational displacements. For the purposes of this research, sea-state 3, 4, and 5 are modeled. Table 2 provides the relevant RMS amplitudes.

11 =

Fig. 5. CVN 65 Carrier Geometry

Lateral Carrier Landing Performance Affecting Factors of Small Carrier-based UAV 535

Table 2. RMS Amplitudes for Modeled Sea-states

Sea-state s s s sx sy sh 3 0.763 0.21 0.12 0.838 0.53 2.11 4 1.22 0.33 0.30 1.4 0.84 3.81 5 1.83 0.49 0.29 2.1 1.26 5.06

The total influence of ship motion for all three modeled sea-states is presented in Figure 7. The landing area lateral displacement is graphed in meter versus time.

4.2 Airwake

Due to the special atmospheric conditions when carrier landing, the MIL-F-8785C military specification gives specific airwake model. The lateral component is composed of two parts: the free atmosphere turbulence component and wake random component. The detailed calculation process can see reference [14] .Take sea state 3 as an example, Set UAV speed 0 30 /V m s= , wind of deck / 15m/sw dV = , initial distance 0 1800D m= , glide slop angle 0 3.5 = , and Get total lateral airwake as shown in figure 8.

4.3 Navigation Error

The Joint Precision Automated Landing System (JPALS) serves as the guidance system on first generation shipboard UAV. JPALS operates using differential-GPS data, blended with inertial navigation on both the airplane and the carrier. The proximity of the ship and airplane during approach and landing cause both GPS receivers to experience the same atmospheric disturbance, resulting in very tight error bounds. NAVAIR provides one and a half hours of JPALS flight test data sampled at 50 Hz. with both the measured and the true position. Figure 9 shows navigation error.

0 20 40 60 80 100 120 140 160 180 200-2

-1

0

1

2

3

t(s)

late

ral d

ispla

cem

ent o

f DTP

(m)

sea-state3sea-state4sea-state5

Fig. 6. Lateral displacement of DTP due to ship motion

536 F. Zheng et al.

0 50 100 150 200-2

-1

0

1

2

t(s)

late

ral w

ind

dist

urb

ance

(m/s

)

0 50 100 150 200-1

-0.5

0

0.5

1

t(s)N

avag

atio

n e

rror

/m

Fig. 7. Lateral airwake simulation Fig. 8. navigation error simulation

4.4 Performance Analysis

It is important to understand the effects of disturbance sources on landing performance. To do this, one thousand simulations are run with each source of noise individually. All other simulations are run with all three sources of error turned on. Table 3 shows these simulation results. Where, Nav denotes navigation error added. Note that these boarding rates account only for landing position, do not account for the possibility of extreme touchdown attitudes at which arrestment would be unsuccessful. In addition, there are many unpredictable factors in the actual UAV landing not taken into account in the simulation. Thus, simulations narrow landing deviation of the allowable range, if deviation exceeds 2 m, the landing will be assumed unsuccessful.

Table 3. Combination Effects of Disturbance Sources

Case Condition Lateral Mean(m)

Lateral Std.Dev(m)

Lateral

Deviation(m) Boarding rate(%)

1 Nav -0.0246 0.1350 -0.3381~0.4262 100 2 Airwake(15*) -0.0672 0.3261 -0.8554~0.9661 100 3 Airwake(15), nav -0.0731 0.3864 -0.9321~1.0342 100 4 Airwake(20), nav -0.0921 0.3709 -1.0432~0.9774 100 5 Seastate3 0.1832 0.4207 -1.2610~1.3827 100 6 Seastate3, Airwake(15), nav 0.2005 0.4516 -1.4832~1.6528 100 7 Seastate3, Airwake(20), nav -0.2752 0.4842 -1.3400~1.8978 100 8 Seastate4 0.4185 0.5905 -1.8232~2.678618 93 9 Seastate4, Airwake(15), nav 0.4231 0.6010 -2.0927~2.9137 92 10 Seastate4, Airwake(20), nav 0.4312 0.5872 -2.1346~2.8033 92 11 Seastate5 0.4890 0.7321 -4.8764~4.3919 86 12 Seastate5, Airwake(15), nav 0.5021 0.7138 -5.0739~5.2138 84 13 Seastate5, Airwake(20), nav 0.4976 0.7872 -5.0655~5.8321 85

*the value of WOD(wind of deck), for example: Airwake(15) denotes the value of airwake when WOD=15m/s.

Lateral Carrier Landing Performance Affecting Factors of Small Carrier-based UAV 537

Each interference would impact on landing performance. Navigation error has negligible influence, on the case of navigation error only, landing mean deviation and standard deviation are very small. However, ship motion and airwake have a great influence on landing performance.

From the data, we can see that ship motion is the dominant source of landing error. The landing performance of sea-state 4 only is worse than Sea-state 3 added air wake and navigation error. Sea-state 5 is the most demanding case because of the magnitude of the required flight path changes. The deck moves as much as 1.5 meter laterally in ten seconds, challenging the systems ability to track a command.

Additionally, simulations show that on all conditions the lateral mean and standard deviation can meet the requirements. On sea-state 3, landing deviation range can also meet requirement. Sea-state 4 and 5 landing deviation ranges exceed the target, and have lower success rate.

5 Performance Analysis with Lateral Attributes Change

Study the impact of the stability derivatives Cy Cl Cn on small UAVs landing performance. Note that no attempt is made to optimize LQR controller performance by tuning Q and R. The purpose is to ensure that the effects of airframe attributes are not masked by variances in controllers.

5.1 Influence of Side Force Coefficient with Sideslip Angle and Rolling Moment Coefficient with Sideslip Angle

The combined influence of side force coefficient with sideslip angle Cy and rolling moment coefficient with sideslip angle Cl is evaluated by a combined total of three hundred thousand simulation runs. The Cy is varied from -0. 4 rad

-1 to -0.04 rad-1 in

increments of 0.04 rad-1. The Cl is varied from -0.1 to -0.01 in increments of 0.01. At each combination, one thousand simulated landings are performed for each sea-state, and at each simulation, airwake and navigation error are added.

Finally, simulation indicates that the boarding rates of sea-state 3 for all modelled configurations are more than 65% . This means that if landing position is the only criterion, the autonomous system would have an acceptable boarding rate for all conditions up to and including sea-state3.

The boarding rate for sea-states 4 and 5 are plotted below in Figures10, each has Cy increasing up the y-axis and Cl increasing along the x-axis.

538 F. Zheng et al.

5055 5560 60 6065 65 6570 70 7075 75 75

80 80 80

80

85

85 85 85

90 9090 90

95

95

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sea-state4

-0.1 -0.08 -0.06 -0.04 -0.02-0.4

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

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-0.1 -0.08 -0.06 -0.04 -0.02-0.4

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

45

50

55

60

65

70

75

80

85

90

Cl

Cy

Fig. 9. Sea-state 4 and sea-state 5 boarding rate contour

Figure 10 illustrates the boarding rate ranges depend on the aircraft aerodynamic parameters. In general, Cy is negative, and higher Cy provides higher boarding

rates, A Cy of 0.15 rad-1

or greater is required for target performance. While Cl has little effect.

Boarding rates are strongly related to the landing dispersion. Figure 11 depicts the landing dispersions for the sea-state4 and sea-state 5. Recall the target performance presented in Table 1 is a standard deviation of 0.9m or less. Figure11 demonstrates the minimum acceptable Cy

is consistent with the above requirement imposed by boarding rate.

0.4

0.50.50

.5

0.5

0.60.60.6

0.70.70.70.80.80.8 0

.90.90.9 111 1.1

1.1 1.2

0.5

1.2

sea-state4

-0.1 -0.08 -0.06 -0.04 -0.02-0.4

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

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0.6

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0.8

0.80.90.

90.90.911111.11.11.1

1.21.21.2 1.31.3

1.3 1.41.4

0.7

1.4 1.51.5

0.7

0.7

0.6

sea-state5

-0.1 -0.08 -0.06 -0.04 -0.02-0.4

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0.6

0.8

1

1.2

1.4

1.6

Cl

Cy

Fig. 10. sea-state4 and sea-state 5 landing position error standard deviation contour

Lateral Carrier Landing Performance Affecting Factors of Small Carrier-based UAV 539

5.2 Influence of Side Force Coefficient with Sideslip Angle and Yawing Moment Coefficient with Sideslip Angle

The effect of yawing moment coefficient with sideslip angle coefficient Cn on landing performance is evaluated from 0.01rad-1 to 0.1rad-1 in increments of 0.01 rad-1. The Cy is varied in exactly the same manner as for the previous section, but Cl

is held constant ( 0.0598Cl = rad-1). Again, one thousand simulations are conducted for each aircraft configuration.

Figure 12 depicts the resultant landing dispersions of sea-state5. As in the previous section, boarding rates are above 75% for all configurations at sea-state3. Landing performance is again highly dependent on Cy , which improves for increasing Cy . Varying Cn has minimal effects on landing performance. But, form the figure, we can find that the landing performance has an worse trend with increasing Cn

.

50 5055 55 5560

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11 1.11.11.1 1.2

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0.02 0.04 0.06 0.08 0.1-0.4

-0.35

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-0.25

-0.2

-0.15

-0.1

-0.05

0.6

0.8

1

1.2

1.4

1.6

Cn

Cy

Fig. 11. Sea-state 5 boarding rate and landing position error standard deviation contour

6 Conclusions

Factors affecting lateral carrier landing performance of small UAV are studied. The results indicate that: Ship motion is the dominant cause of landing errors. Landing dispersion and boarding rates are highly dependent on sea-state. Rolling moment and yawing moment coefficient with sideslip angle coefficient have negligible influence on landing precision. Side force coefficient with sideslip angle is the dominant aircraft attribute for all landing performance criteria and each improves with increasing side force coefficient with sideslip angle. The minimum absolute value of side force coefficient with sideslip for suitable lateral landing performance is 0.15 per radian.

540 F. Zheng et al.

References 1. Sierra Nevada Corporation. UAV common automatic recovery system (UCARS),

http://www.sncorp.com/prod/cnsatm/uav/uav1.html (accessed April 25, 2006)

2. Zhao, T.: Development of the Shipborne UAVS. Ship Electronic Engineering 30(4), 2124 (2010)

3. Steinberg, M.: Development and simulation of an F/A-18 fuzzy logic automatic carrier landing system. In: IEEE International Conference on Fuzzy Systems Proceedings, vol. 2, pp. 797802 (1993)

4. Sousa, P., Wellons, L., Colby, G., et al.: Test Results of an F/A-18 Automatic Carrier Landing Using Shipboard Relative Global Positioning System, Report No. NAWCADPAX /RTR-2003/122, Naval Air Warfare Center Aircraft Division, Patuxent River, MD, 9 (2003)

5. Lizarraga, M.I.: Autonomous landing system for a UAV. Naval Postgraduate School, Monterey (2004)

6. Liu, Q., Yuan, S.Z.: Longitudinal carrier landing system design for UAV based on Journal of Anhui University (Natural Science Edition) 35(1), 4751 (2011)

7. Nicholas, A.: Denison. Automated Carrier Landing of an Unmanned Combat Aerial Vehicle Using Dynamic Inversion. Department of The Air Force University, Ensign (2007)

8. Yang, Y.D.: Translation set of carrier aircraft landing technology. National Defence Industry Press (2003)

9. Sweger, M.J.F.: Design specifications development for unmanned aircraft carrier landings. United States Naval Academy Annapolis, United States (2003)

10. Rudowsky et al.: Review of the Carrier Approach Crite ria for Carrier-Based Aircraft Phase I; Final Report. Report number: NAWCADPAX/TR-2002/71. Naval Air Systems Command (2002)

11. Yang, Y.D., Yu, J.Y.: Guidance and control of carrier aircraft aircraft landing. National Defence Industry Press (2007)

12. Flying Qualities of Piloted Aircraft. Military Specification MIL-F-8785C. United States Department of Defence (November 1980)

13. Nelson, R.C.: Flight Stability and Automatic Control. McGraw-Hill Book Company (1998)

14. Jiao, X.: Research on carrier-based aircraft landing condition and wave-off decision technology. Nanjing University of Aeronautics and Astronautics (2011)

Lateral Carrier Landing Performance Affecting Factorsof Small Carrier-Based UAV1 Introduction2 Small UAV Lateral Automatic Carrier Landing System3 Lateral Control System3.1 Small Carrier-Based UAV Modelling3.2 Improved LQR Flight Controller Design

4 Performance Analysis with Disturbance Sources4.1 Ship Dynamic4.2 Airwake4.3 Navigation Error4.4 Performance Analysis

5 Performance Analysis with Lateral Attributes Change5.1 Influence of Side Force Coefficient with Sideslip Angle and Rolling Moment Coefficient with Sideslip Angle5.2 Influence of Side Force Coefficient with Sideslip Angle and Yawing Moment Coefficient with Sideslip Angle

6 ConclusionsReferences