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Medium and Low-Level Energy Saving Control Strategies for Electric-Powered UAVs

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Flight endurance of an electric-powered Unmanned Aerial Vehicle (UAV) is restricted by its limited on-board energy, which might endanger the accomplishment of any mission it has been sent on. Therefore, the vehicle’s energy consumption should be reduced as much as possible to prolong its flight. This paper deals with the minimization of the energy consumed by an electric-powered UAV as it tracks a desired target point by designing the necessary energy saving control techniques. Two approaches are proposed. The first one addresses the energy consumption problem by optimizing the UAV’s low-level controller. The second approach makes use of the powerful Artificial Bee Colony algorithm to optimize the medium-level controller which guarantees not only target point tracking but also minimization of energy consumption. The effectiveness of both proposals is validated by simulations on MATLAB/Simulink.
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Unmanned Aerial Vehicle; Energy Optimization; Artificial Bee Colony algorithm; PID Controller Tuning

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H. Shakhatreh et al., Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges, IEEE Access, Vol. 7:48572-48634, 2019.

Uc Ríos, C., Teruel, P., Use of Unmanned Aerial Vehicles for Calibration of the Precision Approach Path Indicator System, (2021) International Review of Aerospace Engineering (IREASE), 14 (4), pp. 192-200.

Salman, S., Al Dhaheri, M., Dawson, P., Anavatti, S., Autonomous Water Sampling Payload Design, (2020) International Review of Aerospace Engineering (IREASE), 13 (3), pp. 120-125.

Alnuaimi, M., Perhinschi, M., Al-Sinbol, G., Immunity-Based Framework for Autonomous Flight in GNSS-Denied Environment, (2019) International Review of Aerospace Engineering (IREASE), 12 (6), pp. 239-249.

F. Morbidi, R. Cano and D. Lara, Minimum-Energy Path Generation for a Quadrotor UAV, IEEE International Coference on Robotics and Automation, Stockholm, Sweden, May 2016.

S. Driessens and P. Pounds, The Triangular Quadrotor: A More Efficient Quadrotor Configuration, IEEE Transactions on Robotics, Vol. 31:1517-1526, 2016.

M. Ryll, H. Bulthoff and P. Giordano, A Novel Overactuated Quadrotor Unmanned Aerial Vehicle: Modeling, Control and Experimental Validation, IEEE Transactions on Control Systems Technology, Vol. 23:540-556, 2015.

D. Gurdan, J. Stumpf, M. Achtelik, K. Doth, G. Hirzinger and D. Rus, Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 khz, IEEE International Conference on Robotics ad Automation, Rome, Italy, April 2007.

J.F. Roberts, J.-C. Zufferey and D. Floreano, Energy Management for Indoor Hovering Robots, IEEE/RSJ International Conference on Intelligent Robots ad Systems, Nice, France, September, 2008.

A. Koszewnik and D. Oldziej, Performance assessment of an energy harvesting system located on a copter, The European Physical Journal Special Topics, Vol. 228:1677-1692, 2019.

R. Sowah, M. Acquah, A. Ofoli, G. Mills and K. Koumadi, Rotational Energy Harvesting To Prolong Flight Duration of Quadcopters, IEEE Transactions on Industry Applications, Vol. 53:4965-4972, 2017.

D. Gandolfo, L. Salinas, A. Brandao, and J. Toibero, Path Following for Unmanned Helicopter: An Approach on Energy Autonomy Improvement, Information Technology and Control, Vol. 45:86-98, 2016.

D. Gandolfo, L. Salinas and J. Toibero, Energy Evaluation of Low-Level Cotrol in UAVs powered by Lithium Polymer Battery, ISA Transactions, Vol. 71:563-572, 2017.

A. Chamseddine, Y. Zhang, C.A. Rabbath, C. Join, and D. Theilliol, Flatness-based Trajectory Planning/Replanning for a Quadrotor Unmanned Aerial Vehicle, IEEE Transactions o Aerospace and Electronic Systems, Vol. 48:2832-2848, 2012.

W. Zhang, W. Wang, N. Chen and C. Wang, Efficient UAV path planning with multiconstraints in a 3D large battlefield environment, Mathematical Problems in Engineering, 1-12, 2014.

Y. Zhang, L. Wu and S. Wang, UCAV Path Planning by Flitness-Scaling Adaptive Chaotic Particle Swarm Optimization, Mathematical Problems in Engineering, 2013.

Mlayeh, H., Ghachem, S., Nasri, O., Ben Othman, K., Stabilization of a Quadrotor Vehicle Using PD and Recursive Nonlinear Control Techniques, (2021) International Review of Aerospace Engineering (IREASE), 14 (4), pp. 211-219.

D. Cabecinhas, R. Naldi, L. Marconi, C. Silvestre and R. Cunha, Robust take-off for a Quadrotor Vehicle, IEEE Transactions on Robotics, Vol. 28: 734-742, 2012.

M. Ryll, H. H. Bulthoff, and P. R. Giordano, Modeling and control of a quadrotor UAV with tilting propellers, IEEE International Coference on Robotics and Automation, Saint Paul, MN, USA, May 2012.

S. Kolawole and D. Haibin, Satellite Formation Keeping via Chaotic Artificial Bee Colony, Aircraft Engineering and Aerospace Technology, Vol. 89: 246-256, 2017.

M. Abachizadeh, M. Yazdi, and A. Yousefi-Koma, Optimal Tuning of PID Controllers using Artificial Bee Colony Algorithm, IEEE/ASME International Coference on Advanced Intelligent Mechatronics, Montreal, QC, Canada, July 2010.

S. Bouabdallah, Design and Control of Quadrotors with Application to Autonomous Flying, Ph.D. dissertation, Faculte des Sciences et Techniques, Ecole Polytechnique Federale de Lausanne, Switzerland, 2007.

F. Morbidi, R. Cano, and D. Lara, Minimum-Energy Path Generation for a Quadrotor UAV, IEEE International Coference on Robotics and Automation, Stockholm, Sweden, May 2016.

T. Kim and W. Qiao, A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects, IEEE Transactions on Energy Conversion, Vol. 26: 1172-1180, 2011.

D. Gandolfo, A. Brandao, D. Patino and M. Molina, Dynamic Model of Lithium Polymer Battery-Load Resistor Method for Electric Parameters Identification, Journal of the Energy Institute, Vol. 88: 470-479, 2015.

D. Karaboga and B. Basturk, Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems, International Fuzzy Systems Association World Congress, Cancun, Mexico, June 2007.

D. Karaboga, An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report TR06, Dept. Computer. Eng., Erciyes Univ., Kayseri, Turkey, 2005.


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