Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-14T15:16:49.030Z Has data issue: false hasContentIssue false
  • English
  • Français

Stabilisation, tracking and disturbance rejection control design for the UAS-S45 Bálaam

Published online by Cambridge University Press:  10 March 2022

M.A.J. Kuitche ,
H. Yañez-Badillo ,
R.M. Botez Open the ORCID record for R.M. Botez [Opens in a new window]  and
S.M. Hashemi
M.A.J. Kuitche
Affiliation:
ETS, Laboratory of Active Controls, Avionics and AeroServoElasticity LARCASE, 1100 Notre Dame West, Montreal, QC, Canada, H3C-1K3
H. Yañez-Badillo
Affiliation:
ETS, Laboratory of Active Controls, Avionics and AeroServoElasticity LARCASE, 1100 Notre Dame West, Montreal, QC, Canada, H3C-1K3
R.M. Botez*
Affiliation:
ETS, Laboratory of Active Controls, Avionics and AeroServoElasticity LARCASE, 1100 Notre Dame West, Montreal, QC, Canada, H3C-1K3
S.M. Hashemi
Affiliation:
ETS, Laboratory of Active Controls, Avionics and AeroServoElasticity LARCASE, 1100 Notre Dame West, Montreal, QC, Canada, H3C-1K3
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The stabilisation and control mechanisms of an Unmanned Aerial System (UAS) must be properly designed to ensure acceptable flight performance. During their operation, these mechanisms are subjected to unknown and random environmental effects, making it imperative that all available information should be taken into consideration during the mechanisms’ design process (e.g. system dynamics, actuators, flight conditions and certain criteria requirements such as phugoid and short modes for longitudinal dynamics, and roll subsidence, spiral and Dutch-roll modes for lateral dynamics) in order to guarantee flight stability. Therefore, this paper introduces a novel methodology for the stabilisation and control of the UAS-S45 Bálaam, designed and manufactured by Hydra Technologies. This methodology uses composite controllers that combine feedback Linear Quadratic Regulators (LQR) and Proportional Integral Feed-Forward (PI-FF) compensation controller for stabilisation and tracking tasks, respectively. Furthermore, a Generalised Extended State Observer was implemented to provide robustness to the closed loop dynamics by introducing disturbance compensation. Furthermore, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was adopted to perform a gain scheduling by computing the gains of each composite controller for certain unknown trim conditions within a given flight domain. Finally, several numerical assessments were performed to highlight the efficiency of the proposed methodology.

Keywords

Robust controlsGeneralised Extended State ObserverOptimal controlUAS-S45Disturbance rejectionLQRPI-FFANFIS
Type
Research Article
Information
The Aeronautical Journal , Volume 126 , Issue 1303 , September 2022 , pp. 1474 - 1496
Check for updates
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Eren, U., Prach, A., Koçer, B.B., Raković, S.V., Kayacan, E. and Açıkmeşe, B. Model predictive control in aerospace systems: Current state and opportunities, J. Guid. Control Dyn., 2017, 40, pp 1541–1566. https://doi.org/10.2514/1.G002507 Google Scholar
Valyou, D., Ceruti, A., Miller, J., Pawlowski, B., Marzocca, P. and Tranchitella, M. Design, Optimisation, Performances and Flight Operation of an All Composite Unmanned Aerial Vehicle. SAE International, 2013.Google Scholar
Aubeelack, H. and Botez, R.M. Simulation study of the aerodynamic force distributions on the UAS-S45 Baalam wing with an upswept blended winglet, INCAS Bull., 2019, 11, pp 21–38.Google Scholar
Sugar Gabor, O., Koreanschi, A. and Botez, R.M. Numerical study of UAS-S4 Éhecatl aerodynamic performance improvement obtained with the use of a morphing wing approach, 33rd AIAA Applied Aerodynamics Conference, p 2259, American Institute of Aeronautics and Astronautics, 2015.Google Scholar
Segui, M., Kuitche, M. and Botez, R.M. Longitudinal aerodynamic coefficients of hydra technologies UAS-S4 from geometrical data, AIAA Modeling and Simulation Technologies Conference, p 0579, American Institute of Aeronautics and Astronautics, 2017.Google Scholar
Kammegne, M.J.T., Grigorie, L.T., Botez, R.M. and Koreanschi, A. Design and wind tunnel experimental validation of a controlled new rotary actuation system for a morphing wing application, Proc. Inst. Mech. Eng. G J. Aerosp. Eng., 2016, 230, pp 132–145. https://doi.org/10.1177/0954410015588573 Google Scholar
Botez, R.M., Kammegne, M.J.T. and Grigorie, L.T. Design, numerical simulation and experimental testing of a controlled electrical actuation system in a real aircraft morphing wing model, Aeronaut. J., 2015, 119, pp 1047–1072. https://doi.org/10.1017/S0001924000011131 CrossRefGoogle Scholar
Li, B., Zhang, Y., Ge, Y., Shao, Z. and Li, P. Optimal control-based online motion planning for cooperative lane changes of connected and automated vehicles, Presented at the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC September, 2017.Google Scholar
Frost, S., Taylor, B. and Bodson, M. Investigation of optimal control allocation for gust load alleviation in flight control, Presented at the AIAA Atmospheric Flight Mechanics Conference, Minneapolis, Minnesota, 2012.Google Scholar
Zhen, Z., Jiang, J., Wang, X. and Gao, C. Information fusion based optimal control for large civil aircraft system, ISA Trans., 2015, 55, pp 81–91. https://doi.org/10.1016/j.isatra.2014.09.017 Google ScholarPubMed
Vinodh Kumar, E., Raaja, G.S. and Jerome, J. Adaptive PSO for optimal LQR tracking control of 2 DoF laboratory helicopter, Appl. Soft Comput., 2016, 41, pp 77–90. https://doi.org/10.1016/j.asoc.2015.12.023 Google Scholar
Kálmán, R.E. Contributions to the theory of optimal control, Presented at the Bol. Soc. Mat. Mexicana, 1960.Google Scholar
Doyle, J.C. Guaranteed margins for LQG regulators, IEEE Trans. Automat. Control, 1978, 23, pp 756–757.CrossRefGoogle Scholar
Starr, A.W. and Ho, Y.C. Nonzero-sum differential games, J. Optim. Theory Appl., 1969, 3, pp 184–206. https://doi.org/10.1007/BF00929443 Google Scholar
Rosenbrock, H. and McMorran, P. Good, bad, or optimal? IEEE Trans. Automat. Control, 1971, 16, pp 552–554. https://doi.org/10.1109/TAC.1971.1099822 Google Scholar
Sadeghzadeh, I., Chamseddine, A., Theilliol, D. and Zhang, Y. Linear parameter varying control synthesis : State feedback versus H∞ technique with application to quadrotor UAV, Presented at the 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, FL May, 2014.Google Scholar
Balas, M. and Frost, S. Robust adaptative control with disturbance rejection for linear infinite dimensional systems, AIAA Guidance, Navigation, and Control Conference 2012, 2012.Google Scholar
Boughari, Y., Botez, R.M., Ghazi, G. and Theel, F. Flight control clearance of the Cessna Citation X using evolutionary algorithms, Proc. Inst. Mech. Eng. G J. Aerosp. Eng., 2017, 231, pp 510–532. https://doi.org/10.1177/0954410016640821 CrossRefGoogle Scholar
Liu, X., Sun, Q. and Cooper, J.E. LQG based model predictive control for gust load alleviation, Aerosp. Sci. Technol., 2017, 71, pp 499–509. https://doi.org/10.1016/j.ast.2017.10.006 CrossRefGoogle Scholar
Pavel, M., Shanthakumaran, P., Stroosma, O., Chu, Q., Wolfe, M. and Cazemier, H. Development of advanced flight control laws for the AH-64 Apache helicopter: Sketches from the work of TU Delft-Boeing project in SIMONA simulator, 72nd Annual Forum of the American Helicopter Society: West Palm Beach, USA, 2016.Google Scholar
Pavel, M., Shanthakumaran, P., Chu, Q., Stroosma, O., Wolfe, M. and Cazemier, H. Incremental nonlinear dynamic inversion for the Apache AH-64 helicopter control. J. Am. Helicopter Soc., 2020, 65. https://doi.org/10.4050/JAHS.65.022006 Google Scholar
Simplicio, P., Pavel, M., Van Kampen, E.-J. and Chu, Q. An acceleration measurements-based approach for helicopter nonlinear flight control using incremental nonlinear dynamic inversion, Control Eng. Pract., 2013, 21, pp 1065–1077. https://doi.org/10.1016/j.conengprac.2013.03.009 CrossRefGoogle Scholar
Obinata, G. and Anderson, B.D.O. Methods for Model Reduction. In: Model Reduction for Control System Design. Communications and Control Engineering. pp. 1–59. Springer, London, 2012. https://doi.org/10.1007/978-1-4471-0283-0_1 Google Scholar
Liu, Z.X., Yuan, C., Zhang, Y. and Luo, J. A learning-based fuzzy LQR control scheme for height control of an unmammed quadrotor helicopter, Presented at the 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, FL May, 2014.Google Scholar
Magar, K.T., Balas, M., Frost, S. and Li, N. Adaptive state feedback: Theory and application for wind turbine control, Energies, 2017, 10, p 2145. https://doi.org/10.3390/en10122145 Google Scholar
Ceruti, A., Rossi, V. and Saggiani, G.M. A fuzzy logic autopilot development for a light twin engine aircraft in the approach flight condition, Presented at the ICAS 2002 Congress, 2002.Google Scholar
Hušek, P. and Narenathreyas, K. Aircraft longitudinal motion control based on Takagi–Sugeno fuzzy model, Appl. Soft Comput., 2016, 49, pp 269–278. https://doi.org/10.1016/j.asoc.2016.07.038 CrossRefGoogle Scholar
Duong, M.Q., Grimaccia, F., Leva, S., Mussetta, M. and Ogliari, E. Pitch angle control using hybrid controller for all operating regions of SCIG wind turbine system, Renew. Energy, 2014, 70, pp 197–203. https://doi.org/10.1016/j.renene.2014.03.072 Google Scholar
Duong, M.Q., Grimaccia, F., Leva, S., Mussetta, M. and Le, K.H. Improving transient stability in a grid-connected squirrel-cage induction generator wind turbine system using a fuzzy logic controller, Energies, 2015, 8, pp 6328–6349. https://doi.org/10.3390/en8076328 Google Scholar
Grimaccia, F., Mussetta, M. and Zich, R. Neuro-fuzzy predictive model for PV energy production based on weather forecast, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), pp 2454–2457, 2011.Google Scholar
Wu, D., Chen, M. and Gong, H. Adaptive neural flight control for an aircraft with time-varying distributed delays, Neurocomputing, 2018, 307, pp 130–145. https://doi.org/10.1016/j.neucom.2018.04.038 CrossRefGoogle Scholar
Fareh, R., Al-Shabi, M., Bettayeb, M. and Ghommam, J. Robust active disturbance rejection control for flexible link manipulator, Robotica, 2020, 38, pp 118–135.Google Scholar
Han, J. From PID to active disturbance rejection control, IEEE Trans. Ind. Electron., 2009, 56, pp 900–906.CrossRefGoogle Scholar
Li, S., Yang, J., Chen, W.-H. and Chen, X. Generalized extended state observer based control for systems with mismatched uncertainties, IEEE Trans. Ind. Electron., 2011, 59, pp 4792–4802.CrossRefGoogle Scholar
Fethalla, N., Saad, M., Michalska, H. and Ghommam, J. Robust observer-based dynamic sliding mode controller for a quadrotor UAV, IEEE Access, 2018, 6, pp 45846–45859.Google Scholar
Ghommam, J., Mnif, F. and Derbel, N. Global stabilisation and tracking control of underactuated surface vessels, IET Control Theory Appl., 2010, 4, pp 71–88.Google Scholar
Shi, D., Wu, Z. and Chou, W. Generalized extended state observer based high precision attitude control of quadrotor vehicles subject to wind disturbance, IEEE Access, 2018, 6, pp 32349–32359.CrossRefGoogle Scholar
Pawar, S.N., Chile, R.H. and Patre, B.M. Design of generalized extended state observer based control for nonlinear systems with matched and mismatched uncertainties, 2017 Indian Control Conference (ICC), pp 65–71, IEEE, 2017.Google Scholar
Leggett, D. and Cord, T. Flying qualities demonstration maneuvers, Biennial Flight Test Conference, p 2113, 1994.Google Scholar
Klyde, D.H., Schulze, C.P., Miller, J.P., Manriquez, J.A., Kotikalpudi, A., Mitchell, D.G., Seiler, P.J., Regan, C., Taylor, B. and Olson, C. Defining Handling Qualities of Unmanned Aerial Systems: Phase II Final Report. National Aeronautics and Space Administration, Hampton, Virginia, 2020.Google Scholar
Li, T., Zhang, S., Yang, H., Zhang, Y. and Zhang, L. Robust missile longitudinal autopilot design based on equivalent-input-disturbance and generalized extended state observer approach, Proc. Inst. Mech. Eng. G J. Aerosp. Eng., 2015, 229, pp 1025–1042.Google Scholar
Kang, H.-S., Kim, Y.-T., Hyun, C.-H. and Park, M. Generalized extended state observer approach to robust tracking control for wheeled mobile robot with skidding and slipping, Int. J. Adv. Robot. Syst., 2013, 10, p 155.CrossRefGoogle Scholar
Das, S. and Talole, S.E. GESO based robust output tracking controller for marine vessels, Ocean Eng., 2016, 121, pp 156–165.Google Scholar
Zhou, Y., Huang, Z., Peng, J., Li, H. and Liao, H. A generalized extended state observer for supercapacitor state of charge estimation under disturbances, 2017 American Control Conference (ACC), pp 4029–4034, IEEE, 2017.Google Scholar
Villaseñor, C., Gallegos, A.A., Gomez-Avila, J., López-González, G., Rios, J.D. and Arana-Daniel, N. Environment classification for unmanned aerial vehicle using convolutional neural networks, Appl. Sci., 2020, 10, p 4991. https://doi.org/10.3390/app10144991 Google Scholar
Kuitche, M.A.J. and Botez, R.M. Modeling novel methodologies for unmanned aerial systems: Applications to the UAS-S4 Ehecatl and the UAS-S45 Bálaam, Chinese J. Aeronaut., 2019, 32, pp 58–77. https://doi.org/10.1016/j.cja.2018.10.012 Google Scholar
Anton, N., Botez, R.M. and Popescu, D. Stability derivatives for a delta-wing X-31 aircraft validated using wind tunnel test data, Proc. Inst. Mech. Eng. G J. Aerosp. Eng., 2011, 225, 403–416. https://doi.org/10.1243/09544100JAERO799 Google Scholar
Popescu, D. Nouvelle Implémentation de la Procédure DATCOM pour le Calcul des Coefficients Aérodynamiques et des Dérivées de Stabilité dans le Domaine Subsonique de Vol, http://espace.etsmtl.ca/74/, 2009.Google Scholar
Anton, N., Botez, R. and Popescu, D. New methodologies for aircraft stability derivatives determination from its geometrical data, AIAA Atmospheric Flight Mechanics Conference, p 6046, American Institute of Aeronautics and Astronautics, Chicago, Illinois, 2009.Google Scholar
Mitchell, D.G., Hoh, R.H., Aponso, B.L. and Klyde, D.H. Proposed Incorporation of Mission-Oriented Flying Qualities into MIL-STD-1797A. Systems Technology Inc Hawthorne CA, 1994.Google Scholar
US Military: MIL-F-8785C, Military Specification: Flying Qualities Of Piloted Airplanes, 1980.Google Scholar
Cook, M.V. Chapter 5 - The Solution of the Equations of Motion. In: Flight Dynamics Principles (Third Edition). pp. 109–145. Butterworth-Heinemann, Oxford, 2013. https://doi.org/10.1016/B978-0-08-098242-7.00005-5 Google Scholar
Stevens, B.L. and Lewis, F.L. Aircraft Control and Simulation, John Wiley & Sons, 2003, Hoboken, N.J.Google Scholar
Phillips, W.F. Mechanics of Flight, John Wiley & Sons, Inc, 2009, New Jersey.Google Scholar
Choi, J.W. and Seo, Y.B. LQR design with eigenstructure assignment capability [and application to aircraft flight control, IEEE Trans. Aerosp. Electron. Syst., 1999, 35, pp 700–708. https://doi.org/10.1109/7.766949 Google Scholar
Vepa, R. Flight Dynamics, Simulation, and Control : For Rigid and Flexible Aircraft, CRC Press, 2014, Boca Raton.Google Scholar
Ashraf, A., Mei, W., Gaoyuan, L., Anjum, Z. and Kamal, M.M. Design linear feedback and LQR controller for lateral flight dynamics of F-16 aircraft, 2018 International Conference on Control, Automation and Information Sciences (ICCAIS), pp 367–371, 2018.Google Scholar
Reznik, L., Ghanayem, O. and Bourmistrov, A. PID plus fuzzy controller structures as a design base for industrial applications, Eng. Appl. Artif. Intell., 2000, 13, pp 419–430. https://doi.org/10.1016/S0952-1976(00)00013-0 Google Scholar
Saussié, D., Saydy, L. and Akhrif, O. Longitudinal flight control design with handling quality requirements, Aeronaut. J., 2006, 110, pp 627–637. https://doi.org/10.1017/S0001924000001494 Google Scholar
Li, S., Yang, J., Chen, W. and Chen, X. Generalized extended state observer based control for systems with mismatched uncertainties, IEEE Trans. Ind. Electron., 2012, 59, pp 4792–4802. https://doi.org/10.1109/TIE.2011.2182011 Google Scholar
Rugh, W.J. and Shamma, J.S. Research on gain scheduling, Automatica, 2000, 36, pp 1401–1425. https://doi.org/10.1016/S0005-1098(00)00058-3 Google Scholar
Jang, J.-S.R. ANFIS: Adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man. Cybern., 1993, 23, pp 665–685. https://doi.org/10.1109/21.256541 Google Scholar
Grigorie, T.L., Botez, R.M., Popov, A.V., Mamou, M. and Mébarki, Y. A hybrid fuzzy logic proportional-integral-derivative and conventional on-off controller for morphing wing actuation using shape memory alloy part 1: Morphing system mechanisms and controller architecture design, Aeronaut. J., 2012, 116, pp 433–449. https://doi.org/10.1017/S0001924000006977 Google Scholar
Grigorie, T.L. and Botez, R.M. Adaptive neuro-fuzzy inference system-based controllers for smart material actuator modelling, Proc. Inst. Mech. Eng. G J. Aerosp. Eng., 2009, 223, pp 655–668. https://doi.org/10.1243/09544100JAERO522 CrossRefGoogle Scholar
Grigorie, T.L. and Botez, R.M. Positioning monitoring improvement in a horizontal plane INS by using fuzzy logic data fusion for denoising of inertial sensors in redundant clusters, Int. J. Fuzzy Syst. Adv. Appl., 2015, 2, pp 33–40.Google Scholar
Suparta, W. and Alhasa, K.M. Adaptive Neuro-Fuzzy Interference System. In: Modeling of Tropospheric Delays Using ANFIS, pp. 5–18. Springer International Publishing, 2016, Cham.Google Scholar