AUTHORS: Lucjan Setlak, Rafal Kowalik

Download as PDF

ABSTRACT: It is well known that the process of controlling a rotorcraft with a drive referred to as a rotor aircraft, in the event of adverse weather conditions, e.g. under the influence of strong wind, is the most difficult phase of the flight, requiring a lot of commitment and skill from the pilot/operator. This situation is confirmed by a relatively large number of aviation accidents that occur during the implementation of the process of controlling a multi-rotor in difficult weather conditions. In view of the above, it should be noted that the degree of difficulty of piloting an unmanned aircraft increases significantly when this operation is performed remotely by means of radio signals. As a consequence, the process of safely bringing an unmanned aircraft to the ground is extremely difficult even for an experienced operator who receives limited information about the flight condition of a multi-rotor. In view of the above, it is necessary to implement on-board control systems that enable automatic implementation of the flight stabilization process, e.g. during a storm. The key goal of this work is to design a multi-rotor control system based on the proposed algorithms for controlling unmanned aerial vehicles during high-wind flight, supported by a mathematical apparatus and selected simulation tests in the Matlab/Simulink environment. Based on the above, in the final part of this work, practical conclusions were formulated, reflecting the desirability of the tests carried out and confirmation of the results obtained.

KEYWORDS: Control system, multi-rotor, mathematical analysis, influence of strong wind

REFERENCES:

[1] K.P. Valavanis, G.J. Vachtsevanos, Handbook of Unmanned Aerial Vehicles, Springer: Dordrecht, The Netherlands, 2015.

[2] M. Jafari, H. Xu, L.R.G. Carrillo, Brain Emotional Learning-Based Intelligent Controller for flocking of Multi-Agent Systems. In Proceedings of the American Control Conference (ACC), Washington, DC, USA, 24-26 May 2017; pp. 1996-2001.

[3] M. Jafari, On the Cooperative Control and Obstacle Avoidance of Multi-Vehicle Systems. Master’s Thesis, University of Nevada, Reno, NV, USA, 2015.

[4] A. Nourmohammadi, M. Jafari, T.O. Zander, A Survey on Unmanned Aerial Vehicle Remote Control Using Brain-Computer Interface, IEEE Transactions on Human-Machine Systems, Volume 48, Issue 4, 2018, pp. 337-348.

[5] L. Setlak, R. Kowalik, Examination of the Unmanned Aerial Vehicle, ITM Web of Conferences 24, 01006, 2019.

[6] R.A.S. Fernández, S. Dominguez, P. Campoy, L1 adaptive control for Wind gust rejection in quad-rotor UAV wind turbine inspection. In Proceedings of the 2017 International Conference on Unmanned Aircraft Systems (ICUAS), Miami, FL, USA, 13-16 June 2017, pp. 1840-1849.

[7] A. Cho, J. Kim, S. Lee, and C. Kee, Wind Estimation and Airspeed Calibration using a UAV with a Single-Antenna GPS Receiver and Pitot Tube, IEEE Transactions on Aerospace and Electronic Systems, Vol. 47, Issue 1, 2011, pp. 109-117.

[8] A. Kroonenberg, T. Martin, M. Buschmann, J. Bange, and P. Vorsmann, Measuring the Wind Vector Using the Autonomous Mini Aerial Vehicle M2AV, American Meteorological Society, Vol. 25, 2008, pp. 1969-1982.

[9] M. Allen, and V. Lin, Guidance and Control of an Autonomous Soaring UAV, NASA/TM2007-214611, 2007.

[10] H.S. Choi, S. Lee, J. Lee, E.T. Kim, and H. Shim, Aircraft Longitudinal Auto-landing Guidance Law Using Time Delay Control Scheme, The Japan Society for Aeronautical and Space Sciences, Vol. 53, 2010, pp. 207- 214.

[11] L. Setlak and R. Kowalik, MEMS Electromechanical Microsystem as a Support System for the Position Determining Process with the Use of the Inertial Navigation System INS and Kalman Filter, WSEAS Transactions on Applied and Theoretical Mechanics, Vol. 14, 2019, pp. 105-117.

[12] L.R.G. Carrillo, A.E.D. López,; R. Lozano, C. Pégard, Quad Rotorcraft Control: Vision-Based Hovering and Navigation; Springer: Berlin, Germany, 2012.

[13] S.L. Waslander, J.S. Jang, C.J. Tomlin, MultiAgent X4-Flyer Testbed Control Design: Integral Sliding Mode vs. Reinforcement Learning, IEEE Conference on Intelligent Robots and Systems, 2009.

[14] N. Guenard, T. Hamel, V. Moreau, and S.A. France, Dynamic Modeling and Intuitive Control Strategy for an X4-fyer, in ICCA'05 International Conference on Control and Automation, 2005, pp. 1-6.

[15] M. Jafari, S. Sengupta, H.M. La, Adaptive Flocking Control of Multiple Unmanned Ground Vehicles by Using a UAV. In Advances in Visual Computing: 11th International Symposium, ISVC 2015, Proceedings, Part II, Las Vegas, NV, USA, 14- 16, December 2015.

[16] Y. Zhang, B. Xu, H. Li, Adaptive Neural Control of a Quadrotor Helicopter with Extreme Learning Machine, In Proceedings of ELM-2014; Springer: Berlin, Germany, Vol. 2, 2015, pp. 125-134.

[17] B. Xian, C. Diao, B. Zhao, Y. Zhang, Nonlinear robust output feedback tracking control of a quadrotor UAV using quaternion representation. Nonlinear Dynamics, Vol. 79, 2015, pp. 2735-2752.

[18] G. Bebis, R. Boyle, B. Parvin, D. Koracin, I. Pavlidis, R. Feris, T. McGraw, M. Elendt, R. Kopper, E. Ragan, et al., Eds.; Springer: Cham, Switzerland, 2015; pp. 628-637.

[19] M. Jafari, A.M. Shahri, S.B. Shouraki, Attitude control of a quadrotor using brain emotional learning based intelligent controller. In Proceedings of the 2013 13th Iranian Conference on Fuzzy Systems (IFSC), Tehran, Iran, 27-29 August 2013; pp. 1-5.

[20] S. Lee, J. Lee, and D.S. Lee, Lateral and Directional SCAS Controller Design Using Multidisciplinary Optimization Program, Journal of the Korean Society for Aeronautical & Space Sciences, Vol. 40, Issue 3, 2012, pp. 251-257.

[21] D. N. Axford, On the Accuracy of Wind Measurements Using an Inertial Platform in an Aircraft, and an Example of a Measurement of the Vertical Mesostructure of the Atmosphere, Journal of Applied Meteorology, Vol. 7, 1968, Issue 4, pp. 645-666.

[22] S. Yoon and Y. Kim, Constrained Adaptive Backstepping Controller Design for Aircraft Landing in Wind Disturbance and Actuator Stuck, International Journal for Aeronautical and Space Sciences, 13 (2012), pp. 74-89.

[23] L. Setlak, R. Kowalik, S. Bodzon, The Study of Air Flows for an Electric Motor with a Nozzle for an Unmanned Flying Platform, WSEAS Transactions on Fluid Mechanics, Vol. 14, 2019, pp. 21-35.

[24] L.J. Bjarke, and L.J. Ehernberger, An In-Flight Technique for Wind Measurement in Support of the Space Shuttle Program, NASA TM 4154, 1989, pp. 3-4.

[25] C. Coza, C. Nicol, C. Macnab, A. RamirezSerrano, Adaptive fuzzy control for a quadrotor helicopter robust to wind buffeting, Journal of Intelligent and Fuzzy Systems, Vol. 22, Issue 5, 2011, pp. 267-283.

[26] H. Bou-Ammar, H. Voos, W. Ertel, Controller design for quadrotor UAVs using reinforcement learning. In Proceedings of the 2010 IEEE International Conference on Control Applications (CCA), Tokyo, Japan, 8- 10 September 2010, pp. 2130-2135.

[27] S. Islam, M. Faraz, R. Ashour, G. Cai, J. Dias, L. Seneviratne, Adaptive sliding mode control design for quadrotor unmanned aerial vehicle, In Proceedings of the 2015 International Conference on Unmanned Aircraft Systems (ICUAS), Denver, CO, USA, 9-12 June 2015, pp. 34-39.

[28] J.W. Langelaan, N. Alley, and J. Neidhoefer, Wind Field Estimation for Small Unmanned Aerial Vehicles, AIAA Guidance, Navigation and Control Conference, 2010.

[29] L. Setlak, R. Kowalik, Stability Evaluation of the Flight Trajectory of Unmanned Aerial Vehicle in the Presence of Strong Wind, WSEAS Transactions on Systems and Control, ISSN/E-ISSN: 1991-8763/2224-2856, Volume 14, , Art. #6, pp. 43-50, 2019.

[30] D. Noble, S. Bhandari, Neural network based nonlinear model reference adaptive controller for an unmanned aerial vehicle. In Proceedings of the 2017 International Conference on Unmanned Aircraft Systems (ICUAS), Miami, FL, USA, 13-16 June 2017; pp. 94-103.

[31] L. Setlak, R. Kowalik, Stability Evaluation of the Flight Trajectory of Unmanned Aerial Vehicle in the Presence of Strong Wind, WSEAS Transactions on Systems and Control, Vol. 14, 2019, pp. 51-56.

[32] P.B. Sujit, Srikanth Saripalli, and Joao Borges Sousa, Unmanned aerial vehicle path following: A survey and analysis of algorithms for fixed-wing unmanned aerial vehicles, IEEE Control Systems, 34(1): 2014, pp. 42-59.

[33] N. Michael, D. Melinger, Q. Lindsey, and V. Kumar, The GRASP Multiple Micro UAV Testbed, Robotics & Automation Magazine, IEEE, 2010, pp. 56-65.