Adaptive Algorithm for Fixed Wing UAV Autolanding on Aircraft Carrier

This is the author preprint version, not including the changes arising from the review process, of the following final paper (that should be used for reference and citation): Adaptive Algorithm for Fixed Wing UAV Autolanding on Aircraft Carrier E. De Lellis, V. Di Vito, M. Ruby, N. Salbego AIAA Infotech@Aerospace 2013 Conference, 19-22 August 2013, Boston, USA

Adaptive Algorithm for Fixed Wing UAV Autolanding on Aircraft Carrier De Lellis E.1, Di Vito V.2 CIRA – Italian Aerospace Research Centre, Capua(CE), 81043, Italy Ruby M., Salbego N. French Air Force Academy

This paper presents an adaptive algorithm for autolanding of a fixed wing UAV on an aircraft carrier. The algorithm is characterized by the specific feature of implementing suitable adaptivity in order to improve the ability of a UAV in performing the automatic landing on a moving runway, such as an aircraft carrier. Furthermore, in order to consider the specific requirements and constraints associated to the autonomous approach and landing of a UAV on an aircraft carrier, the algorithm properly takes into account the procedures typically used by Navy’s pilot to land on the aircraft carrier. The paper is structured into several paragraphs describing both the proposed algorithm and its numerical validation results. More in details, in the paper first a summary state-of-the-art of autolanding algorithms is outlined, then the overall Guidance, Navigation and Control system architecture, where the autolanding algorithm is integrated, is introduced and the proposed algorithm is described. The design philosophy is illustrated, which is based on the consideration of the autolanding trajectory generation problem according to two stages: the plan of a nominal trajectory applicable to the ideal case and the periodical regeneration, real time during the mission, of a new trajectory, according to the current state of both vehicle and carrier. In the second part of the paper, finally, the synthetic environment used for the numerical validation of the proposed algorithm is described and the results of the validation campaign are reported and commented, showing the effectiveness of the proposed algorithm even in presence of external disturbances.

I.

I

Introduction

n recent years, several research activities1 have been developed in order to increase the autonomy features in

Unmanned Aerial Vehicles (UAVs), to substitute human pilots in dangerous missions or simply in order to execute specific tasks more efficiently and cheaply. In particular, a significant research effort has been devoted to achieve

high automation in the landing phase, so as to allow the landing of an aircraft without human intervention, also in presence of severe environmental disturbances. Furthermore, the worldwide research community reached a common agreement on the opportunity of the dual use of UAVs, for both military and civil purposes; therefore it is recognized as very important to make the UAVs and their autolanding systems compliant with the current and future regulations. 1 2

Research Engineer, Flight Systems Dept., via Maiorise snc, Capua (CE), 81043, Italy, [email protected]. Research Engineer, Flight Systems Dept., via Maiorise snc, Capua (CE), 81043, Italy, [email protected].

The development of autolanding systems with the desired level of reliability, accuracy and safety involves an evolution of all the subsystems related to the guidance, navigation and control disciplines. The main drawback of the current autolanding systems consists in the lack of “adaptivity” of the trajectory generation and tracking process in order to take into account and properly react to unpredictable external events, such as varied environmental conditions and unexpected threats. Different approaches have been proposed to address the aircraft automatic landing, based on the use of both modern intelligent control techniques and classical control theory2-6. Furthermore, aerospace companies and research centres have developed programs in the field of autonomy in UAVs, also covering the automatic landing issue5-7. The Italian Aerospace Research Center (CIRA) also worked in the last years on the topic of autolanding, in the framework of the research activities carried out to design and implement a complete autonomous system for the whole flight mission execution, covering all the involved flight phases, from take-off to landing including mid-air 3D/4D waypoint navigation and collision avoidance. This overall autonomous system has been developed with reference to the application on fixed wing aircraft. It has been validated through intensive numerical simulation campaigns and through real-time with hardware in the loop laboratory simulations, being finally demonstrated in real operative environment through some flight testing campaigns. With particular reference to the autolanding capability, suitable algorithm has been developed, tested and demonstrated by CIRA. It is able to autonomously real time generate a trajectory compliant with the dynamic constraints acting on the vehicle, performing a fully automatic landing of a fixed wing aircraft starting from any point of the three dimensional space and using the DGPS/AHRS technology. This algorithm, successfully tested by means of several both real time hardware in the loop simulations8 and flight tests9, had still the limitation that the trajectory was generated only once, remaining the same until the touch down without adaptive features. In order to overcome this limitation and to make safer the system, CIRA developed also a fully adaptive autonomous landing algorithm10, featuring the ability of generating on line, with a desired updating rate or at a specified event, the nominal trajectory for the aircraft, based on the current state of the vehicle and on the desired state at touch down point. The purpose of the work presented in this paper is to improve the ability of a UAV in performing the automatic landing on a moving runway, starting from the CIRA autolanding background activities. More in details, in a previous work10, the authors presented the flight test results of the adaptive algorithm for autonomous fixed wing aircraft landing on a fixed runway. Main features of the autolanding system based on the implementation of the

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algorithm proposed in the cited work10 were: on line landing trajectory real time re-planning, full autonomy from pilot’s inputs, ability to use weakly instrumented landing runway, ability to land starting from any point in space and autonomous management of failures and/or adverse atmospheric conditions. In the present paper, the previously proposed algorithm10 is extended in order to deal with the particular and interesting application consisting in the autonomous approach and landing of a UAV on an aircraft carrier, i.e. on a moving runway. To perform this challenging task, it has been necessary to suitably modify the already developed autolanding algorithm10, in order to take into account the procedures typically used by a Navy’s pilot to land on the aircraft carrier and to properly consider the particular constraints related to this situation. The paper is organized as follows. In section II the overall Guidance, Navigation and Control system architecture, where the autolanding algorithm is integrated, is introduced and described by a functional point of view. Then, in the section III, the proposed algorithm is explained, by emphasizing the adopted design philosophy, which is based on the consideration of the autolanding trajectory generation problem according to two stages: the plan of a nominal trajectory applicable to the ideal case, and the periodical regeneration, real time during the mission, of a new trajectory, according to the current state of both vehicle and carrier. Section IV, finally, addresses the description of the synthetic environment used for the numerical validation of the proposed algorithm, whereas the detailed analysis of the simulation results is reported and commented in section V. The validation campaign outcomes show the effectiveness of the proposed algorithm in performing the challenging task of UAV automatic landing on an aircraft carrier, even in presence of external disturbances.

II.

System Architecture

The proposed algorithm for autolanding on an aircraft carrier has been integrated in the overall Guidance, Navigation and Control (GNC) software architecture shown in Figure 1. The architecture comprises the integration of the GNC system with a higher level (from the hierarchical point of view) Decision making module, which is in charge of managing the mission at high level for the specific application of autonomous functions in the UAV, in order to take the decisions to face the unpredictable events affecting the flying platform. Nevertheless, for what concerns the scope of the present paper, focusing on the autolanding algorithm, the core element of the system architecture is the Guidance module, where the algorithm here proposed is integrated. This module elaborates the reference trajectory in accordance with the flight instructions and, for the specific case of the autolanding, it calculates the references describing the generated trajectory for the approach and landing to be

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provided to the lower level flight control system. The landing trajectory is real time generated from the actual position of the UAV up to the target position along the runway of the aircraft-carrier. The algorithm for autolanding integrated in the Guidance module is described in the following section III, with particular reference to the approach and landing flight phases.

Figure 1 – GNC software architecture Nevertheless, from a more general point of view, the autolanding algorithm comprises also the Alignment flight phase, which is preliminary to the approach and landing and is worth to briefly describe here. The Alignment algorithm is implemented in the Guidance module and aims to move the vehicle from any initial condition, in terms of position and velocity, to a proper final condition. This final condition is constituted by a waypoint, specified in terms of three-dimensional position and velocity vector of the vehicle, which is located near the landing runway and is aligned with its centreline. In order to connect the initial position with the final waypoint, a 3D trajectory, constituted at the most by two circular arcs and one straight line, is generated on-line. This trajectory is sub-optimal, in the sense that it is the minimum length trajectory if the vehicle moves only in the horizontal plane but not necessarily it is the minimum length trajectory in the 3D space. The nominal trajectory is generated by solving online a constrained optimization problem, in which suitable constraints on the flight path angle and on the minimum turn radius are considered. The restriction on the minimum turn radius is derived from a proper constraint on the roll angle and from the inertial speed reference imposed to the vehicle. Since the autothrottle is designed in order to track the TAS instead of the inertial speed, in the Alignment phase the inertial speed considered for the trajectory generation is set to a proper safety value. The detailed description of the trajectory generation and tracking

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algorithms for the Alignment phase is not yet the focus of this paper and can be examined in some previous works8-9 of the authors. The Flight Controls module generates the commands towards the UAV control surfaces and throttle actuators. It comprises a Stability and Control Augmentation System (SCAS) and some basic Autopilot control modes. The SCAS control module consists of SISO (Single Input Single Output) control algorithms arranged in a classical yaw damped attitude control scheme, that accepts the pitch and roll angles attitudes as reference signals. All the loops feedback the three angular velocities (pitch, roll and yaw) and the two attitudes angles (pitch and roll), by using only proportional gains in a cascade structure (only for pitch and roll channels). The Autopilot includes an autothrottle for settling the desired vehicle true airspeed (TAS), an altitude controller with vertical velocity inner feedback, a vertical speed controller, a track angle controller in order to set the desired velocity direction in the horizontal plane, a lateral displacement controller for tracking nominal trajectory in the horizontal plane, and a heading angle controller. The Autopilot, based on the selected input configuration, can accept track angle, altitude, vertical speed, TAS, heading and lateral position reference signals from the upper modules implementing the proposed approach and landing algorithm. It then outputs the desired pitch and roll commands to the inner SCAS module. The Decision making, Guidance and Flight controls modules are supported by the Navigation suite and the Situation awareness modules. The first module performs a best estimation of the UAV state using the measures of the navigation sensors (GPS, Laser Altimeter, Inertial Unit, Air Data System), whereas the latter provides all the information about the surrounding environment, including the carrier general state and motion (particularly relevant for the specific application here considered), by means of embedded sensor and a dedicated datalink with the carrier.

III.

Algorithm Description

As partially mentioned in the introduction, the autonomous landing system here proposed preserves all the positive characteristics of the algorithm already presented in a previous work10, as indicated in the following: • fully adaptive, in the sense that it can generate on line, with a desired updating rate or in case of a pre-selected driven event, the nominal trajectory for the aircraft, based on the actual state of the vehicle and on the desired state at touch down point. The fully adaptive generation starts with the Flare phase and is active up to the touch down event; • free path, in the sense that it is able to accomplish the alignment to the assigned runway starting from any initial state, in terms of position and velocity;

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• able to perform the automatic landing manoeuvre using a navigation system constituted by a DGPS/RTK unit and an AHRS unit, so it requires only a weakly instrumented landing runway, which must be only equipped with the differential GPS surface station. These characteristics have been exploited for the application related to an autolanding on a carrier. Also in this case, the autonomous landing process is divided into four main phases, each corresponding to a specific state of the mission automation logic. These main phases are named: Alignment, Approach, Flare and Pre-Touch Down. The Alignment and Pre-Touch Down phases are identical to the ones descripted in the previous work already cited10, so their description is here omitted for the sake of brevity (nevertheless, some information about the Alignment phase has been provided in the previous section II), while the approach and flare phases have been modified to enable an autolanding on a carrier. The Guidance module has been developed with the aim to properly generate the references towards the Flight Control module in terms of vertical speed and velocity references. The desired profiles of altitude, vertical speed and velocity of the UAV are illustrated in Figure 2. All the adopted symbols will be explained in the following.

Figure 2 – Nominal profiles of altitude, vertical speed and velocity

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The general aim to perform an autolanding on a moving carrier is a 4D trajectory problem. With respect to a classical autolanding problem, it is important also the timing of the generated trajectory. For this reason it has been more profitable to consider simultaneously the trajectory generation for the phases of ramp and flare. The study reported in this paper is divided in a first part considering the development of a convenient nominal trajectory, and a second part and third parts facing the problem of updating the trajectory in real time considering the real measures collected during the ramp and flare phases. Moreover, it has been faced the problem of generating the right references towards the Flight controls module. The trajectory structure, involving the phases of ramp and flare, adopted as nominal case is shown in Figure 3. The nominal trajectory is elaborated during the phases preceding the ramp and the formal equations utilized for the trajectory are similar to that already formulated in a previous work10.

Figure 3 – Nominal trajectory The trajectory equations have been written under the following assumptions:

a1. The movement of the carrier is only on the longitudinal axis. a2. There is no pitch movement of the carrier and therefore the height of the deck is constant. a3. The speed of the carrier is constant. a4. The current position and the speed of the carrier are known.

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Nominal trajectory generation problem The problem of finding the nominal trajectory during the phases preceding the final approach considers as known variables the 4D waypoint for the desired touchdown (i.e. the vector representing the position of the desired touchdown of the UAV on the carrier and the desired time for this touchdown). Considering the assumption a1, it is possible to use a 2D reference system with x axis along the longitudinal axis of the carrier and with the altitude axis along the vertical axis (Up) of a classical Nord-East-Up reference system. The two axes (x, h) adopted are shown in Figure 3. The problem can be faced assuming as known variables the desired values at the touchdown in terms of longitudinal position, altitude, time, vertical speed and inertial velocity of the UAV (xf, hf, tf, Vzf, Vaf), the peculiar vertical speed and inertial velocity of the UAV during the ramp phase (Vz0, Va0), and finally the peculiar altitude over the deck at the beginning of the ramp phase and of the flare phase (h0, h1). The unknown variables are the longitudinal position of the UAV and the time at the beginning of the ramp phase (x0, t0) and at the beginning of the flare phase (x1, t1). Considering x as independent variable of the several considered profiles (e.g. vertical speed profile, velocity profile, and altitude profile already illustrated in Figure 2), the following equations formalize the carrier kinematic behaviour (1) and the aircraft kinematic behaviour during the ramp phase (2) and during the flare phase (3).

xf − xc 0 = Vc × (tf − t 0)

Vz ( x ) = V z 0

(1)

(2)

Vx ( x ) = Vx 0

Vz ( x ) = a ( x − x f ) + b

(3)

Vx ( x ) = c ( x − x f ) + d To solve the kinematic problem above mentioned (i.e. to find the unknown variables starting from the known variables) in an analytic way it is necessary a further assumption:

a5. Constant Wind and Vx>>Vz (e.g. Vx almost equal to Va).

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This approximation is justified by the fact that the slope angle is between -1 deg and -4 deg during ramp and flare phases. Therefore the equations (4) can be used to find the unknown variables of the problem. This assumption allows solving the problem of finding a flare trajectory compliant with the profiles of equations (3) and with all the boundary condition. The first two equations in (4) are written on the basis of the UAV kinematic behavior during the ramp phase. The third and the fourth equations in (4) are written solving the kinematic problem regarding the UAV during the flare phase, considering the linear profiles of equations (3) and the known variables above mentioned as boundary conditions (here it is exploited the analytic solution already found in a previous work10).

t 0 = t1 +

h 0 − h1 Vz 0

x 0 = x1 − Va 0 × (t1 − t 0)

t 1 = tf −

xf − x1  Vaf  ⋅ ln  Vaf − Va 0  Va 0 

x1 = x f +

(4)

( h1 − hf ) ⋅ (Va 0 − Vaf ) Vaf Vaf  Vaf  (1 + ⋅ ln( )) ⋅ (Vz 0 − Vzf ) − Vzf ⋅ ln  Va 0 − Vaf Va 0  Va 0 

The equations (5) can be used to find, in the phases preceding the ramp, the 4D waypoint (x0, h0, 0, t0) to be used as reference for the Navigation system of the UAV. This elaboration can be updated with a fixed frequency rate using the current Vc and assuring to select a feasible touchdown 4D waypoint (xf, hf, 0, tf) taking into consideration the expected positions of carrier and UAV. Some variables of the nominal trajectory will be used in the next phases: the nominal distance of the flare phase
xnom=xf-x1, the nominal duration of the flare phase
tnom=tf-t1, the nominal velocity and nominal vertical speed during the ramp.

∆xnom =

∆tnom =

( hf − h1) ⋅ (Va 0 − Vaf ) Vaf Vaf  Vaf  (1 + ⋅ ln( )) ⋅ (Vz 0 − Vzf ) − Vzf ⋅ ln   V a 0 − Vaf Va 0  Va 0 

∆xnom  Vaf  ⋅ ln  Vaf − Va 0  Va 0 

(5)

Va 0 nom = Va 0

Vz 0 nom = Vz 0

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On-line trajectory generation problem during ramp phase As soon as the ramp phase starts, the trajectory generation problem changes point of view. In this phase, the trajectory can be generated step by step or on predefined events or prefixed rate. During the ramp phase the final aim of the Guidance module is to generate references towards the Flight Controls module in terms of vertical speed and TAS. For each trajectory generation, the current longitudinal position and altitude of the UAV and the current longitudinal position and velocity of the carrier are measures available from the Navigation Suite and Situational Awareness modules and can be considered as known variables (x0, h0, xc0, Vc, t0). Moreover, other known variables are the desired altitude over the deck used as threshold for starting the flare phase (h1) and the desired parameters at the touchdown in terms of vertical speed, inertial velocity and altitude of the UAV (Vzf, Vaf, hf). During the ramp phase, the unknown variables are time and longitudinal positions at the end of the ramp and at the end of the flare (tf, xf, t1, x1) and the vertical speed and the inertial velocity of the UAV to hold during the ramp (Vz0, Va0). A design choice for the current problem solution has been using as constraints the previously elaborated nominal distance and duration of the flare phase (
xnom,
tnom). The rationale of this choice is to make adaptive the ramp phase solving the problem in such a way to make as nominal as possible the following flare phase; indeed, the most demanding manoeuvre for the Flight Control module is that performed during the flare phase. Therefore equations (6) can be used to find the unknown variables of the problem. The first equation in (6) is written on the basis of the kinematic behaviour of the carrier, the second and third ones are written exploiting the above mentioned design choice. The fourth equation is written to assure the continuity of inertial velocity between ramp and flare phases (note that assuming a nominal distance and a nominal duration of the flare phase is equivalent to affirm that the first values of the vertical speed and inertial velocity profiles during the flare are the nominal ones Vz0nom, Va0nom). The fifth and the sixth equations are written on the basis of the kinematic behaviour of the UAV during the ramp phase. Note that the vertical speed during the ramp phase is used as control variable to capture the 4D waypoint (x1, 0, h1, t1), while the initial vertical speed assumed for the flare profile is Vz0nom. Because of this reason, the vertical speed will not be continuous between the ramp and flare phases as cleared in Figure 4 depicting the altitude, velocity and vertical speed profiles during the two phases. Nevertheless, in case of ideal control during the phase preceding the ramp and during the ramp, the real trajectory would be equal to the nominal one and also the vertical speed would be continuous. During the ramp phase the two control variable will be Vz0 and Va0.

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xf = Vc × (t 0 − tf ) − xc 0

t1 = t f − ∆tnom x1 = x f − ∆xnom

(6)

Va 0 = Va 0 nom x1 − x0 = Va 0 (t1 − t0 ) h1 − h0 = Vz 0 (t1 − t0 )

Figure 4 – Real reference profiles of altitude, vertical speed and velocity On-line trajectory generation problem during flare phase When the flare phase starts (i.e. when the UAV reaches an altitude over the deck equals to h1), the problem changes again. Once again the final aim of the Guidance module is to generate vertical speed and velocity reference towards the Flight controls module and an adaptive flare trajectory has to be generated with a fixed rate. For each trajectory generation, the current longitudinal position and altitude of the UAV and the current longitudinal position and velocity of the carrier are measures available from the Navigation Suite and Situational Awareness modules and

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can be considered as known variables (x0, h0, xc0, Vc, t0). Moreover, other known variables are the desired parameters at the touchdown in terms of vertical speed and altitude (Vzf, hf) and the frozen inertial velocity profile shown in Figure 4 (the related frozen parameters are Vaf and Va0nom). During the flare phase, the unknown variables are the time and the longitudinal position at the touchdown (tf, xf) and the initial value of the flare vertical speed profile (Vz0). Therefore equations (7) can be used to find the unknown variables of the problem. The first equation in (7) is written on the basis of the kinematic behaviour of the carrier, the second and third ones are written on the basis of the kinematic problem regarding the UAV during the flare phase, considering the linear profiles of equations (3) and the known variables above mentioned as boundary conditions (here it is exploited the analytic solution already found in a previous work10).

xf = Vc × (t 0 − tf ) − xc 0 Vaf  − ( hF   Va 0 

Vzf ( x0 − x f ) ln

Vz 0 = Vzf +



Vaf



(Va 0 − Vaf )

( x0 − x f ) 1 +

t f − t0 =

( x f − x0 ) Vaf − Va 0

− h0 )(Va 0 − Vaf ) Vxf     Va 0  

ln

(7)

 Vaf  ⋅ ln   Va 0 

Assuming to re-generate the trajectory each time step, the value Vz0 can be used directly as vertical speed reference towards the Flight control module. Instead the nominal velocity profile (Va profile shown in Figure 2) can be used to elaborate the velocity reference towards Flight control module also during the flare phase. During both the ramp and flare phases, the inertial velocity reference is used to generate a TAS (True Air Speed) reference exploiting the assumption a5 and elaborating the related TAS value compliant with the inertial velocity value and with the last estimation of the wind frozen before generating the trajectory.

IV.

Synthetic Environment for Numerical Validation

In order to perform the numerical validation of the proposed algorithm, the whole GNC system shown in Figure 1, whose architecture has been described in section II, has been implemented in an overall synthetic software

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environment. This environment, as well as the proposed algorithm and the overall GNC system, has been developed in Matlab/Simulink and includes: • the model of the UAV, • the model of the aircraft carrier, • the model of the outside world (i.e. other than the carrier) comprehensive of sea and air, • the model of the particular subsystems used as aids in the manned aircraft landing, like arresting wires and optical systems. The modeling study considered also the Navy’s pilot procedure in approaching a carrier11 and the physical constraints due to approach and landing on a particular carrier as the French Porte-avions “Charles de Gaulle”12. The validation of the algorithm has been addressed by assuming some simplifying hypotheses: • the wind is constant and there are no gusts, • the problem is considered only on the longitudinal axis, • the runway is considered on the axis of the aircraft-carrier, • the motion is only along the longitudinal plane of the aircraft carrier, with the UAV already aligned along the carrier centreline. The above indicated assumptions led to some consequences for what concerns the synthetic environment used for the algorithm validation. For what concerns the adopted UAV model, the assumptions above indicated allowed using a simplified model for the vehicle, consisting in a point of mass model describing the UAV motion by means of three degrees of freedom. For what concerns the model of the aircraft carrier, differently from conventional runways the aircraft-carrier is moving. It moves on the sea and the assumed hypotheses allowed considering here that this movement is only on the longitudinal axis, because the lateral motion of the aircraft carrier during the landing phase is considered small. Moreover, the carrier movement has been considered at a constant speed, which is justified by the fact that the inertia of the carrier is very important and in these conditions the change of speed are relatively slow compared to the final trajectory of the UAV. Furthermore, the aircraft-carrier has a rolling movement; nevertheless, thanks to the proper carrier stabilization system, this movement is expected to not exceed + or – 3 deg, being so negligible12. This motivates the circumstance that in the synthetic environment the roll movement of the carrier has been not

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considered. Finally, the aircraft carrier can have also a pitch angle movement and only 1 deg of pitch angle increase leads to an increase of 2 meters of the deck at the beginning of the runway. The period of this movement is around 10s, depending on the aircraft-carrier. A variation excessively important means a stronger impact if the deck is going up and also a modification of the point of impact on the longitudinal axis. This modification is explained by the fact that if the deck is higher than previously, the contact will be earlier, so the UAV will touch the carrier before the nominal estimated point. Nevertheless, the pitch movement is quite a complex phenomenon to take into account, so the simplifying hypothesis has been assumed in the synthetic environment that the carrier pitch movement is negligible and, consequently, the runway altitude is constant. For what concerns the wind model, finally, the assumed hypotheses led to the implementation in the synthetic environment of a wind oriented exclusively on the longitudinal direction of both UAV and carrier, without any cross wind component. The wind model allowed in any case the simulation of turbulences and wind gusts, always in the longitudinal direction of course, with different magnitudes.

V.

Numerical Validation Results

In this section, the results of laboratory real-time validation by means of hardware-in-the-loop simulations are illustrated. The following figures show some simulation results, namely: • the position along the x longitudinal axis of the carrier (referred as “porte avion”) of the UAV (referred as “avion”) and the position of the carrier versus time (indicated as “temps”) during ramp and flare phases; • a zoom of the previous graph during the last seconds before touch down; • the altitude of the UAV and of the carrier versus time during ramp and flare phases; • a zoom of the previous graph during the last seconds before touch down; • the true airspeed feedback (indicated as “reel”) and reference control value (indicated as “reference”) of the UAV versus time; • the vertical speed feedback (indicated as “reel”) and reference control value (indicated as “reference”) of the UAV versus time More in details, in the following figures the results are shown obtained from the simulation of the basic case (no wind), and of the cases including the presence of severe wind turbulence and wind gust. In the following table the parameters used for simulations configuration are indicated.

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PARAMETER Inertial velocity of the-carrier [m/s] Initial longitudinal position of the carrier [m] Altitude above the deck of the UAV at the beginning of the ramp phase [m] Altitude above the deck of the UAV at the beginning of the flare phase [m] Altitude above the sea of the deck [m] Vertical speed of the UAV during the ramp [m/s] Inertial velocity of the UAV during the ramp [m/s] Desired vertical speed of the UAV at touchdown [m/s] Desired inertial velocity of the UAV at touchdown [m/s] Desired longitudinal position along X axis

SYMBOL Vc xc0 h0 h1 n/a Vz0 Va0 Vzf Vaf xf

VALUE 10 0 60 15 40 -2 30 -0.3 25 0

Table 1 - Parameters for simulations configuration

Figure 5 –Basic case: carrier moving and no wind over the sea

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Figure 6 – Basic case plus constant wind

Figure 7 – Basic case plus constant wind and turbulence

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Figure 8 – Basic case plus wind, turbulences and wind gust The touchdown performances achieved in terms of longitudinal position, vertical speed and velocity are all inside the typical requirements for this kind of application. The average performances achieved at the touchdown event are summarized in the following Table 2. The analysis has been carried out varying some environmental parameters, as indicated in Table 3. VARIABLE Longitudinal position along X axis Vertical speed of the UAV at touchdown [m/s] Inertial velocity of the UAV at touchdown [m/s]

SYMBOL xf Vzf Vaf

AVG VALUE 1.5 -0.9 24.5

Table 2 – Average performances of the main variables at the touchdown

PARAMETERS Front wind Rear wind gust

MIN VALUE 0 m/s 0 m/s

MAX VALUE 5 m/s 2 m/s

Table 3 – Environmental parameters for performances analysis

VI.

Conclusions

The paper presented an adaptive algorithm for fixed wing UAV autolanding on a moving aircraft carrier and the results obtained from its validation by means of specific numerical simulation campaign have been reported and discussed, showing the effectiveness of the proposed algorithm. The algorithm has been designed in order to provide

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a UAV with an autolanding system featuring the needed adaptivity in order to manage the challenging task of landing on an aircraft carrier. The algorithm has been implemented in Matlab/Simulink and several simulations, including real-time with hardware in the loop validation, have been performed, by considering suitable modeling of the UAV, of the carrier and of the environment. It has been observed that with constant wind, or without wind, the longitudinal precision at touchdown was around a meter, so allowing the aircraft to catch the arrestor wire. Furthermore, the vertical speed resulted very close to the expected one, so indicating that the algorithm performs very well in presence of wind. Performances resulted to be affected by turbulences and windgusts, nevertheless even in presence of these disturbances the performances resulted quite good. As emphasized in the paper, at the current stage of development the proposed system is based on some simplifying hypotheses, which need to be removed in the next version of the algorithm. Therefore, future work will cover some key points not yet implemented in the proposed algorithm. The consideration of the vertical movement of the runway, because of the modification of the carrier pitch angle, will be taken into account, as well as the carrier vertical speed. Furthermore, the problem will be extended to the consideration of a 3D environment, including the transversal movement of the aircraft, the cross wind, and the fact that on almost all the carriers there is an angle of ten degrees between the runway axis and the carrier axis.

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”Porte-avions Charles de Gaulle : Caractéristiques principales”, http://www.netmarine.net/bat/porteavi/cdg/caracter.htm ,

seen the 29th July 2013

Biographies Ettore De Lellis – Project manager at Italian Aerospace Research Centre (CIRA), On Board Systems and ATM Research Unit, since 2010. Senior research engineer in Guide, Navigation and Control department of CIRA since 2002 up to 2009. His research and development activities are in the field of autonomous guidance algorithms and SW integration. Moreover his technical skills are In the following fields: flight data analysis systems and techniques, advanced automation and control systems design .technologies for manned and unmanned avionic systems, method and technologies for control system rapid prototyping, real-time modeling for HW-in-the-loop simulations. He received his cum laude master degree in electronic engineering from University of Pisa in 2001 and his Ph.D. in aerospace engineering from University “Federico II” of Naples in 2011. Vittorio Di Vito – Research engineer at Italian Aerospace Research Centre (CIRA), since 2009 in the Air Traffic Management Dept. and from 2004 to 2008 in the Flight Systems Dept. His research and development activities are in the field of autonomous flight systems development, with particular emphasis on navigation through 3D and 4D waypoints, self-separation and collision avoidance. He participated in the EU projects PPLANE and 4DCo-GC and in the EDA project MIDCAS. He has been also member of the GARTEUR task forces FM-AG 18 Towards greater Autonomy in Multiple Unmanned Air Vehicles (since 2009) and FM-AG 14 Autonomy in UAVs (2004-2007). Author of many papers on both scientific journals and conference proceedings, he previously carried out research activities in the field of Power Systems analysis and optimization at University of Cassino (Italy), Industrial Engineering Dept., and also worked as Professor of Electrical Engineering at Nautical School of the Italian Financial Police. He received his Ph.D. in Electrical Engineering and his cum laude master degree in Electrical Engineering from University of Cassino, in 2005 and 2001 respectively.

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