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A Fault-Tolerant Control Scheme for Fixed-Wing UAVs with Flight Envelope Awareness

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Abstract

In this work a vertically integrated fault-tolerant control scheme for fixed-wing Unmanned Aerial Vehicles (UAVs) is presented. At its core, an online approximate Trim Flight Envelope generator yields the motion constraints of the UAV. Given fault information, it remains always up-to-date in view of emerging faults. The controller stack comprises of Nonlinear Model Predictive Controllers for angular velocity, linear velocity and position. Path Planning is achieved by Simple Sparse Rapidly-exploring Random Trees (SST). Both the controllers and the planner are aware of the flight constraints and are hence tolerant to faults. A large set of sensor and actuator faults, common to UAVs are considered and the controllability of the UAV is examined. Detailed simulations using real-time implementations of the controllers are carried out.

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Code Availability

The software of the presented controllers can be found at: https://github.com/Georacer/uav_ftc/tree/master

The software of the simulation environment can be found at: https://github.com/Georacer/last_letter

Abbreviations

FE:

Flight Envelope

AoA:

Angle of Attack

AoS:

Angle of Sideslip

LOC:

Loss of Control

UAV:

Unmanned Aerial Vehicle

UAS:

Unmanned Aerial System

AHRS:

Attitude and Heading Reference System

MEMS:

Miniature Electro-Mechanical System

MPC:

Model Predictive Controller

NMPC:

Nonlinear Model Predictive Controller

EKF:

Extended Kalman Filter

RRT:

Rapidly-exploring Random Trees

SST:

Sparse Stable RRT

PIC:

Pilot In Command

FTC:

Fault-Tolerant Control

References

  1. Andrade, R., Raffo, G.V., Normey-Rico, J.E.: Model predictive control of a tilt-rotor UAV for load transportation. European Control Conference, ECC 2016 2165–2170 (2016)

  2. Avram, R.C., Zhang, X., Muse, J.: Quadrotor actuator fault diagnosis and accommodation using nonlinear adaptive estimators. IEEE Trans. Contr. Sys. Tech. 25(6), 2219–2226 (2017)

    Article  Google Scholar 

  3. Bateman, F., Noura, H., Ouladsine, M.: Fault Diagnosis and Fault-Tolerant Control Strategy for the Aerosonde UAV. IEEE Transa. Aerospace Elect. Sys. 47(3), 2119–2137 (2011)

    Article  Google Scholar 

  4. Beard, R.W., Timothy, W.M.: Small Unmanned Aircraft: Theory and Practice. Princeton University Press, Princeton (2012)

    Book  Google Scholar 

  5. Berber, R., Kravaris, C.: Nonlinear Model Based Process Control. Springer, Netherlands (1998)

    Book  Google Scholar 

  6. Brandt, J., Selig, M.: Propeller Performance Data at Low Reynolds Numbers. In: 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition number January. American Inst. Aeronautics Astronautics, pp. 1–18, Reston (2011)

  7. Chen, H., Allgöwer, F.: A Quasi-Infinite Horizon Nonlinear Model Predictive Control Scheme with Guaranteed Stability. Automatica 34(10), 1205–1217 (1998)

    Article  MathSciNet  Google Scholar 

  8. Chen, Y., Wang, C.: A Robust Path Following Controller for a Small Unmanned Aerial Vehicle with Constrained Parameters Optimized. In: Proceedings - 2016 3rd International Conference on Information Science and Control Engineering (ICISCE), vol. 2016, pp. 789–793 (2016)

  9. De Almeida, F.A.: Reference Management for Fault-Tolerant Model Predictive Control. J. Guid. Contro Dynam. 34(1), 44–56 (2011)

    Article  Google Scholar 

  10. De Almeida, F.A., Leißling, D.: Fault-Tolerant Model Predictive Control with Flight-Test Results. J. Guid. Control Dynam. 33(2), 363–375 (2010)

    Article  Google Scholar 

  11. Ducard, G., Rudin, K., Omari, S., Siegwart, R.: Strategies for sensor-fault compensation on UAVs: Review, discussions & additions. In: 2014 European Control Conference (ECC), pp. 1963–1968. IEEE (2014)

  12. Dughman, S.S., Rossiter, J.A.: A survey of guaranteeing feasibility and stability in MPC during target changes. IFAC-PapersOnLine 28(8), 813–818 (2015)

    Article  Google Scholar 

  13. Elgersma, M.R., Morton, B.G.: Nonlinear Six-Degree-of-Freedom Aircraft Trim. J. Guid. Control Dynam. 23(2), 305–311 (2000)

    Article  Google Scholar 

  14. Ferramosca, A., Limon, D., Alvarado, I., Alamo, T., Camacho, E.F.: MPC for tracking of constrained nonlinear systems. In: Proceedings of the IEEE Conference on Decision and Control, pp. 7978–7983 (2009)

  15. Ferramosca, A., Limon, D., Alvarado, I., Alamo, T., Castaṅo, F., Camacho, E.F.: Optimal MPC for tracking of constrained linear systems. Int. J. Syst. Sci. 42(8), 1265–1276 (2011)

    Article  MathSciNet  Google Scholar 

  16. Fridovich-Keil, D., Herbert, S.L., Fisac, J.F., Deglurkar, S., Claire, J.T.: Planning, Fast and Slow: A Framework for Adaptive Real-Time Safe Trajectory Planning. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 387–394. IEEE (2018)

  17. Goman, M.G., Khramtsovsky, A.V., Kolesnikov, E.N.: Evaluation of Aircraft Performance and Maneuverability by Computation of Attainable Equilibrium Sets. J. Guid. Control Dynam. 31(2), 329–339 (2008)

    Article  Google Scholar 

  18. Houska, B., Ferreau, H.J., Diehl, M.: ACADO Toolkit – An Open Source Framework for Automatic Control and Dynamic Optimization. Optimal Control Applications and Methods 32(3), 298–312 (2011)

    Article  MathSciNet  Google Scholar 

  19. Izadi, H.A., Zhang, Y., Gordon, B.W.: Fault Tolerant Model Predictive Control of Quad-Rotor Helicopters with Actuator Fault Estimation. IFAC Proceedings Volumes 44(1), 6343–6348 (2011)

    Article  Google Scholar 

  20. Jung, Y., Bang, H.: Mars precision landing guidance based on model predictive control approach. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 230 (11), 2048–2062 (2016)

    Article  Google Scholar 

  21. Kale, M.M., Chipperfield, A.J.: Stabilized MPC formulations for robust reconfigurable flight control. Control Engineering Practice 13(6), 771–788 (2005)

    Article  Google Scholar 

  22. Karimi, H.R., Hu, X., Guo, Y., Wu, L.: Model predictive control-based non-linear fault tolerant control for air-breathing hypersonic vehicles. IET Control Theory & Applications 8(13), 1147–1153 (2014)

    Article  Google Scholar 

  23. Keviczky, T., Balas, G.J.: Software-Enabled Receding Horizon Control for Autonomous Unmanned Aerial Vehicle Guidance. J. Guid. Control Dynam. 29(3), 680–694 (2006)

    Article  Google Scholar 

  24. Kim, K., Kim, S., Suk, J., Ahn, J., Kim, N., Kim, B.S.: Flight test of flying-wing type unmanned aerial vehicle with partial wing-loss. Proceedings of the Institution of Mechanical Engineers, Part G : Journal of Aerospace Engineering 233(5), 1611–1628 (2019)

    Article  Google Scholar 

  25. Kohler, J., Moller, M.A., Allgower, F.: Nonlinear Reference Tracking with Model Predictive Control:An Intuitive Approach. 2018 European Control Conference, ECC 2018, 1355–1360 (2018)

    Article  Google Scholar 

  26. Kouvaritakis, B., Cannon, M.: Model Predictive Control. Springer, Berlin (2015)

    MATH  Google Scholar 

  27. Kufoalor, D.K., Johansen, T.A.: Reconfigurable fault tolerant flight control based on Nonlinear Model Predictive Control. In: 2013 American Control Conference, pp. 5128–5133 (2013)

  28. LaValle, S.M., Kuffner, J.J.: Randomized Kinodynamic Planning . The International Journal of Robotics Research 20(5), 378–400 (2001)

    Article  Google Scholar 

  29. Leutenegger, S., Siegwart, R.Y.: A low-cost and fail-safe inertial navigation system for airplanes. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 612–618 (2012)

  30. Li, Y., Littlefield, Z., Bekris, K.E.: Asymptotically optimal sampling-based kinodynamic planning. arXiv:1407.2896 (2014)

  31. Lombaerts, T., Schuet, S., Wheeler, K., Acosta, D.M., Kaneshige, J.: Safe Maneuvering Envelope Estimation based on a Physical Approach. In: AIAA Guidance, Navigation, and Control (GNC) Conference, Reston. American Institute of Aeronautics and Astronautics, pp. 1–20 (2013)

  32. Lygeros, J.: On reachability and minimum cost optimal control. Automatica 40(6), 917–927 (2004)

    Article  MathSciNet  Google Scholar 

  33. Mahony, R., Euston, M., Kim, J., Coote, P., Hamel, T.: A non-linear observer for attitude estimation of a fixed-wing unmanned aerial vehicle without GPS measurements. Transactions of the Institute of Measurement and Control 33(6), 699–717 (2011)

    Article  Google Scholar 

  34. Mayne, D.Q.: Optimization in Model Based Control. IFAC Proceedings Volumes 28(9), 229–242 (1995)

    Article  Google Scholar 

  35. Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.M.: Constrained model predictive control: Stability and optimality. Automatica 36(6), 789–814 (2000)

    Article  MathSciNet  Google Scholar 

  36. McDonough, K., Kolmanovsky, I.: Fast Computable Recoverable Sets and Their Use for Aircraft Loss-of-Control Handling. Journal of Guidance Control and Dynamics 40(4), 934–947 (2017)

    Article  Google Scholar 

  37. Mehra, R.K., Wasikowski, M., Prasanth, R.K., Bennett, R.L., Neckels, D.: Model predictive control design for XV-15 tilt rotor flight control. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, number August, Reston, Virigina. American Institute of Aeronautics and Astronautics (2001)

  38. Merheb, A.-R., Bateman, F., Noura, H., Younes, Y.A.: Hierarchical fault-tolerant control of a quadrotor based on fault severity. In: 2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol), volume 2016-Novem, pp. 666–671. IEEE (2016)

  39. Michalska, H., Mayne, D.Q.: Robust receding horizon control of constrained nonlinear systems. IEEE Transactions on Automatic Control 38(11), 1623–1633 (1993)

    Article  MathSciNet  Google Scholar 

  40. Mueller, M.W., D’Andrea, R.: Stability and control of a quadrocopter despite the complete loss of one, two, or three propellers. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 45–52. IEEE (2014)

  41. Nabi, H.N., Lombaerts, T., Zhang, Y., van Kampen, E., Chu, Q.P., de Visser, C.C.: Effects of Structural Failure on the Safe Flight Envelope of Aircraft. Journal of Guidance Control and Dynamics 41(6), 1257–1275 (2018)

    Article  Google Scholar 

  42. Sørensen, M.E.N., Hansen, S., Breivik, M., Blanke, M.: Performance Comparison of Controllers with Fault-Dependent Control Allocation for UAVs. Journal of Intelligent & Robotic Systems 87(1), 187–207 (2017)

    Article  Google Scholar 

  43. Norouzi, R., Kosari, A., Sabour, M.H.: Real time estimation of impaired aircraft flight envelope using feedforward neural networks. Aerospace Science and Technology 90, 434–451 (2019)

    Article  Google Scholar 

  44. Notter, S., Heckmann, A., Mcfadyen, A., Gonzalez, F.: Modelling, Simulation and Flight Test of a Model Predictive Controlled Multirotor with Heavy Slung Load. IFAC-PapersOnLine 49(17), 182–187 (2016)

    Article  Google Scholar 

  45. Petkar, S., Umbarkar, S., Mejari, M., Singh, N.M., Kazi, F.: Robust tube based MPC for PVTOL trajectory tracking using systems flatness property. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), vol. 2016, pp. 1095–1101 (2016)

  46. Quigley, M., Conley, K., Gerkey, B.P., FAust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., Berger, E., Wheeler, R., Mg, A.: ROS: An open-source Robot Operating System. In: ICRA Workshop on Open Source Software, vol. 3, p 5 (2009)

  47. Ruan, X., Hou, X.: Nonlinear predictive decoupling control of Rotorcraft UAV using serial decoupling method. Applied Mechanics and Materials 241-244, 1240–1247 (2013)

    Article  Google Scholar 

  48. Santoso, F., Garratt, M.A., Anavatti, S.G., Petersen, I.: Robust Hybrid Nonlinear Control Systems for the Dynamics of a Quadcopter Drone. In: IEEE Transactions on Systems Man, and Cybernetics: Systems, pp. 1–13 (2018)

  49. Sarunic, P., Evans, R.: Hierarchical model predictive control of UAVs performing multitarget-multisensor tracking. IEEE Transactions on Aerospace and Electronic Systems 50(3), 2253–2268 (2014)

    Article  Google Scholar 

  50. Sarunic, P.W.: Hierarchical Model Predictive Control of an Unmanned-Aerial-Vehicle Based Multitarget-Multisensor Data Fusion System, Ph.D Thesis (March 2012)

  51. Scholte, E., Campbell, M.E.: Robust Nonlinear Model Predictive Control With Partial State Information. IEEE Transactions on Control Systems Technology 16(4), 636–651 (2008)

    Article  Google Scholar 

  52. Simon, D., Lofberg, J., Glad, T.: Reference tracking MPC using terminal set scaling. In: Proceedings of the IEEE Conference on Decision and Control, pp. 4543–4548 (2012)

  53. Stapel, J., de Visser, C.C., Van Kampen, E.-J., Chu, Q.P.: Efficient Methods for Flight Envelope Estimation through Reachability Analysis. In: AIAA Guidance, Navigation, and Control Conference, number January. American Institute of Aeronautics and Astronautics (2016)

  54. Stevens, B.L., Lewis, F.L., Johnson, E.N.: Aircraft Control and Simulation. Number 9. Wiley, 3 edition, 2016.

  55. Şucan, I.A., Moll, M., Kavraki, L.E.: The Open Motion Planning Library. IEEE Robotics & Automation Magazine 19(4), 72–82 (2012). http://ompl.kavrakilab.org

    Article  Google Scholar 

  56. Sun, S., Baert, M., van Schijndel, B.S., de Visser, C.: Upset Recovery Control for Quadrotors Subjected to a Complete Rotor Failure from Large Initial Disturbances. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4273–4279. IEEE (2020)

  57. Vaiopoulos, P., Zogopoulos-Papaliakos, G., Kyriakopoulos, K.J.: Online Aerodynamic Model Identification on Small Fixed-Wing UAVs with Uncertain Flight Data. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 6587–6592. IEEE (2018)

  58. van Oort, E.R., Chu, Q.P., Mulder, J.A.: Maneuver Envelope Determination through Reachability Analysis, pp. 91–102. Springer, Berlin (2011)

    Google Scholar 

  59. Wang, L., Tang, W., Wang, Y., Liang, J., Li, Z., Cui, M.: A comparative study of gust load alleviation of UAV based on LPV and MPC controller. In: Chinese Control Conference, CCC, pp. 8091–8096 (2019)

  60. Zhang, Y.M., Chamseddine, A., Rabbath, C.A., Gordon, B.W., Su, C.-Y., Rakheja, S., Fulford, C., Apkarian, J., Gosselin, P.: Development of advanced FDD and FTC techniques with application to an unmanned quadrotor helicopter testbed. Journal of the Franklin Institute 350(9), 2396–2422 (2013)

    Article  Google Scholar 

  61. Zhou, Q.-L., Zhang, Y., Rabbath, C.-A., Theilliol, D.: Design of feedback linearization control and reconfigurable control allocation with application to a quadrotor UAV. In: 2010 Conference on Control and Fault-Tolerant Systems (SysTol), vol. 514, pp. 371–376. IEEE (Oct 2010)

  62. Zogopoulos-papaliakos, G., Kyriakopoulos, K.J.: A Flight Envelope Determination and Protection System for Fixed-Wing UAVs. In: 2020 International Conference on Unmanned Aircraft Systems (ICUAS) (2020)

  63. Zogopoulos-Papaliakos, G., Kyriakopoulos, K.J.: A Flight Envelope Determination and Protection System for Fixed-Wing UAVs. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 9599–9605. IEEE (2020)

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Funding

This research work was partially funded by the “Hellenic Civil Unmanned Arial Vehicle (HCUAV)” project, sponsored by the Greek Secretariat of Research and Technology.

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Authors and Affiliations

Authors

Contributions

All authors whose names appear on the submission

1. made substantial contributions to the conception and design of the work as well as the creation of new software used in the work;

2. drafted the work or revised it critically for important intellectual content;

3. approved the version to be published; and

4. agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to George Zogopoulos-Papaliakos.

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Appendices

Appendix A: Aircraft Model Coefficients

Key:

 

nominal

Nominal, fault-free model.

pert-5

Based off nominal, all coefficients perturbed by 5%.

ail-elev-50

50% reduction in aileron and elevator efficiency.

ail-0

Total loss of aileron efficiency.

rud-0

Total loss of rudder efficiency.

elev-0

Total loss of elevator efficiency.

mot-0

Total loss of propulsion thrust.

aero-small

Small, manageable fault in aerodynamics.

aero-large

Large, detrimental fault in aerodynamics.

one-ail

One aileron stuck.

Table 5 Table with aircraft models coefficients

Appendix B: Simulations Index

Sim Index

Highest reference

FE enabled

FE used

Ref. Used

Model name

Controller

Notes

01

Linear velocity

Yes

nominal

1

nominal

MPC

 

02

Linear velocity

No

 

1

nominal

MPC

 

03

waypoints

Yes

nominal

2

nominal

MPC

 

04

waypoints

No

 

2

nominal

MPC

 

05

angular velocity

N/A

 

3

nominal

MPC

 

06

angular velocity

N/A

 

3

Pert_5

MPC

5% perturbation in model parameters

07

Linear velocity

Yes

nominal

1

Pert_5

MPC

5% perturbation in model parameters

08

angular velocity

N/A

 

3

nominal

MPC

windy conditions

09

Linear velocity

Yes

nominal

1

nominal

MPC

windy conditions

10

Start-finish

Yes

nominal

4

nominal

MPC

 

11

angular velocity

N/A

 

5

nominal

MPC

 

12

angular velocity

N/A

 

5

Ail_Elev_50

MPC (fault unaware)

50% loss of control surface

       

efficiency

13

angular velocity

N/A

 

5

Ail_Elev_50

MPC (fault aware)

50% loss of control surface

       

efficiency

14

Start-finish

N/A

 

4

Ail_0

Rudder for yaw

100% loss of aileron efficiency

15

waypoints

Yes

rud_0

2

Rud_0

MPC (fault aware)

100% loss of rudder efficiency

16

Start-finish

Yes

 

4

Elev_0

Throttle for pitch

100% loss of elevator efficiency

17

Linear velocity

Yes

mot_0

1

mot_0

MPC (fault aware)

100% loss of thrust

29

angular velocity

N/A

 

3

aero_small

MPC (fault aware)

Small aerodynamics damage

30

angular velocity

N/A

 

3

aero_small

MPC (fault unaware)

Small aerodynamics damage

31

angular velocity

N/A

 

3

aero_large

MPC (fault aware)

Large aerodynamics damage

33

angular velocity

N/A

 

3

one_ail

MPC (fault aware)

Only one aileron operational

34

Linear velocity

Yes

nominal

1

nominal

MPC

Airspeed sensor fault

35

Linear velocity

Yes

nominal

1

nominal

MPC

AoA estimation using [33]

36

Linear velocity

Yes

nominal

1

nominal

MPC

AoA estimation using proposed

       

estimator

38

Linear velocity

Yes

nominal

1

nominal

MPC

Same as 01 but with wind gusts.

39

Start-finish

Yes

mot_0

N/A

mot_0

N/A (Planner only)

Emergency landing

40

Start-finish

Yes

left_turn

N/A

left_turn

N/A (Planner only)

Planning with only left turns

Appendix C: Index of Scenario References

Reference Index

Reference Description

1

Demanding trajectory (velocity) profile

2

Back-and-forth waypoints on different

 

altitudes

3

Demanding angular rate profile

4

Extensive waypoint mission

5

Mild angular rate profile

Glossary

b

wingspan

c

mean chord

\(c_{\left \{ \cdot \right \}}\)

aerodynamic coefficient

C T

Propeller thrust coefficient

D

propeller diameter

δ a

aileron input

δ e

elevator input

δ F

thrust input

δ t

throttle input

δ r

rudder input

J A R

propeller advance ratio

l

aerodynamic rolling moment

m

aerodynamic pitching moment

m

aircraft mass

n

aerodynamic yawing moment

p n

position North

p e

position East

p d

position down

q

pitch rate

\(\bar {q}\)

dynamic pressure

r

yaw rate

S

wing surface area

V a

airspeed

w n

wind North

w e

wind East

w d

wind down

α

angle of attack

β

angle of sideslip

γ

flight path angle

Γ ,J y

inertia tensor coefficients

𝜃

pitch angle

ϕ

roll angle

ψ

yaw angle

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Zogopoulos-Papaliakos, G., Karras, G.C. & Kyriakopoulos, K.J. A Fault-Tolerant Control Scheme for Fixed-Wing UAVs with Flight Envelope Awareness. J Intell Robot Syst 102, 46 (2021). https://doi.org/10.1007/s10846-021-01393-3

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  • DOI: https://doi.org/10.1007/s10846-021-01393-3

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