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BFA fuzzy logic based control allocation for fault-tolerant control of multirotor UAVs

Published online by Cambridge University Press:  26 July 2019

M. Saied Open the ORCID record for M. Saied [Opens in a new window] ,
M. Knaiber ,
H. Mazeh ,
H. Shraim  and
C. Francis
M. Saied*
Affiliation:
Faculty of Engineering, Lebanese University, Scientific Research Center in Engineering (CRSI)Beirut, Lebanon
M. Knaiber
Affiliation:
Faculty of Engineering, Lebanese University, Scientific Research Center in Engineering (CRSI)Beirut, Lebanon
H. Mazeh
Affiliation:
Faculty of Engineering, Lebanese University, Scientific Research Center in Engineering (CRSI)Beirut, Lebanon
H. Shraim*
Affiliation:
Faculty of Engineering, Lebanese University, Scientific Research Center in Engineering (CRSI)Beirut, Lebanon
C. Francis*
Affiliation:
Faculty of Engineering, Lebanese University, Scientific Research Center in Engineering (CRSI)Beirut, Lebanon
*
majd.elsaied@hotmail.com
hassan.shraim@ul.edu.lb
cfrancis@ul.edu.lb
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Abstract

This paper deals with the problem of fault-tolerant control (FTC) for redundant multirotor unmanned aerial vehicles (UAVs) subject to actuators failures. A fuzzy logic approach is used to solve the constrained control allocation problem by adjusting the components of the multiplexing vector once a motor failure is detected. This fuzzy logic allocation problem is tuned using the Bacterial Foraging Algorithm (BFA), a powerful bio-inspired optimisation technique. The effectiveness of this approach is illustrated through real experimental application to a hexarotor UAV, where up to two motors failures are considered.

Keywords

Unmanned Aerial VehiclesFault Tolerant ControlFuzzy Logic
Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1267 , September 2019 , pp. 1356 - 1373
Copyright
© Royal Aeronautical Society 2019 

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References

REFERENCES

Rasmussen, J., Nielsen, J., Garcia-Ruiz, F., Christensen, S. and Streibig, J.C. Potential uses of small unmanned aircraft systems (UAS) in weed research, Weed Research, 2013, 53, (4), pp. 242–248.CrossRefGoogle Scholar
Fink, J., Michael, N., Kim, S. and Kumar, V. Planning and control for cooperative manipulation and transportation with aerial robots, Robotics Research. Springer Tracts in Advanced Robotics, 2011, 70.CrossRefGoogle Scholar
D’Andrea, R. Can drones deliver?, IEEE Transactions on Automation Science and Engineering, 2014, 11, (3), pp. 647–648.CrossRefGoogle Scholar
Chamseddine, A., Zhang, Y., Rabbath, C., Join, C. and Theilliol, D. Flatness-based trajectory planning/replanning for a quadrotor unmanned aerial vehicle, IEEE Transactions on Aerospace and Electronic Systems, 2012, 48, (4), pp. 2832–2848.CrossRefGoogle Scholar
Ranjbaran, M. and Khorasani, K. Fault recovery of an under-actuated quadrotor aerial vehicle, IEEE Conference on Decision and Control, Atlanta, GA, USA, 2010, pp. 4385–4392.CrossRefGoogle Scholar
Sadeghzadeh, I., Chamseddine, A., Zhang, Y. and Theilliol, D. Control allocation and reallocation for a modified quadrotor helicopter against actuator faults, IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, Mexico, 2012, pp. 247–252.Google Scholar
Mueller, M.W. and D’Andrea, R. Relaxed hover solutions for multicopters: application to algorithmic redundancy and novel vehicles, International Journal of Robotics Research, 2016, 35, (8), pp. 873–889.CrossRefGoogle Scholar
Lippiello, V., Ruggiero, F. and Serra, D. Emergency landing for a quadrotor in case of a propeller failure: a backstepping approach, IEEE International Conference on Intelligent Robots and Systems, Chicago, IL, USA, 2014, pp. 4782–4788.Google Scholar
Schneider, T., Ducard, G., Rudin, K. and Pascal, S. Fault-tolerant control allocation for multirotor helicopters using parametric programming, International Micro Air Vehicle Conference and Flight Competition, Germany, 2012.Google Scholar
DU, G.X., Quan, Q. and Cai, K.Y. Controllability analysis and degraded control for a class of hexacopter, Journal of Intelligent and Robotic Systems, 2015, 78, (1), pp. 143–157.CrossRefGoogle Scholar
Falconi, G.P. and Holzapfel, F. Adaptive fault tolerant control allocation for a hexacopter system, IEEE American Control Conference (ACC), Boston, MA, USA, 2016, pp. 6760–6766.Google Scholar
Marks, A., Whidborne, J.F. and Yamamoto, I. Control allocation for fault tolerant control of a VTOL octorotor, UKACC International Conference on Control, Cardiff, UK, 2012, pp. 357–362.Google Scholar
Merheb, A., Noura, H. and Batemann, F. Active fault tolerant control of octorotor uav using dynamic control allocation, International Conference on Intelligent Unmanned Systems, Montreal, Quebec.Google Scholar
Alwi, H. and Edwards, C. LPV sliding mode fault tolerant control of an octorotor using fixed control allocation, Conference on Control and Fault-Tolerant Systems, 2013, Nice, France, 2014, pp. 772–777.Google Scholar
Benazzouz, Y., Aktouf, O. and Parissis, I. A fault fuzzy-ontology for large scale fault-tolerant wireless sensor networks, Procedia Computer Science, 2014, 35, pp. 203–212.CrossRefGoogle Scholar
Shen, Q., Jiang, B. and Cocquempot, V. Fuzzy logic system-based adaptive fault-tolerant control for near-space vehicle attitude dynamics with actuator faults, IEEE Transactions on Fuzzy Systems, 2013, 21, (2), pp. 289–300.CrossRefGoogle Scholar
Huo, B., XIA, Y., Yin, L. and FU, M. Fuzzy adaptive fault-tolerant output feedback attitude-tracking control of rigid spacecraft, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47, (8), pp. 1898–1908.CrossRefGoogle Scholar
Das, S., Biswas, A., Dasgupta, S. and Abraham, A. Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications, Foundations of Computational Intelligence. Studies in Computational Intelligence, 2009, 3, (2).CrossRefGoogle Scholar
Bouabdallah, S. Design and control of quadrotors with application to autonomous flying. Ph.D. thesis, Ecole Polytechnique Federale de Lausanne, 2007.Google Scholar
Michieletto, G., Ryll, M. and Franchi, A. Control of statically hoverable multi-rotor aerial vehicles and application to rotor-failure robustness for hexarotors, IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017.Google Scholar
Sontag, E.D. Kalman’s controllability rank condition: from linear to nonlinear, Antoulas, A.C. (eds) Mathematical System Theory. Springer, Berlin, Heidelberg, 1991, pp. 453–462.CrossRefGoogle Scholar
Brammer, R.F. Controllability in linear autonomous systems with positive controllers, SIAM Journal on Control and Optimization, 1972, 10, (2), pp. 339–353.CrossRefGoogle Scholar
Johansen, A. and Fossen, T. Control allocation – a survey, Automatica, 2013, 49, (5), pp. 1087–1103.CrossRefGoogle Scholar
Lee, C. Fuzzy logic in control systems: fuzzy logic controllers–part I, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1990, 20, (2), pp. 404–418.CrossRefGoogle Scholar