Adaptive Traction Drive Control Algorithm for Electrical Energy Consumption Minimisation of Autonomous Unmanned Aerial Vehicle

Aleksandr Korneyev; Mikhail Gorobetz; Ivars Alps; Leonids Ribickis

doi:10.2478/ecce-2019-0009

Abstract

The paper aims at researching and developing an adaptive control system algorithm and its implementation and integration in the control system of the existing unmanned aerial vehicle (UAV). The authors describe the mathematical model of UAV and target function for energy consumption minimisation and possible searching algorithms for UAV optimal control from an energy efficiency perspective. There are two main goals: to minimise energy consumption and to develop and investigate an adaptive control algorithm for UAV traction drive in order to increase energy efficiency.

The optimal control algorithm is based on two target function values, when comparing and generating corresponding control signals. The main advantage of the proposed algorithm is its unification and usability in any electrical UAV with a different number of traction drives, different or variable mass and other configuration differences without any initial manual setup. Any electric UAV is able to move with maximal energy efficiency using the proposed algorithm.

References

[1] G. Strupka, and I. Raņķis, “Investigation of Unmanned Aerial Vehicle Routes to Fulfill Power Line Management Tasks,” in Chen J. (eds) Advances in Human Factors in Robots and Unmanned Systems. AHFE 2018. Advances in Intelligent Systems and Computing, vol. 784. Springer, Cham., 2019. https://doi.org/10.1007/978-3-319-94346-6_610.1007/978-3-319-94346-6_6
Search in Google Scholar Back to article
[2] N. Bezzo, et al., “Online planning for energy-efficient and disturbance-aware UAV operations,” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Dec. 2016. https://doi.org/10.1109/IROS.2016.775973810.1109/IROS.2016.7759738
Search in Google Scholar Back to article
[3] J. T. Economou, G. Kladis, and A. Tsourdos, “UAV optimum energy assignment using Dijkstra’s Algorithm,” 2007 European Control Conference (ECC), IEEE Xplore, 26 March 2015. https://doi.org/10.23919/ECC.2007.706835310.23919/ECC.2007.7068353
Search in Google Scholar Back to article
[4] L. Li, J. Wu, Y. Xu, J. Che, and J. Liang, “Energy-controlled optimization algorithm for rechargeable unmanned aerial vehicle network,” 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Feb. 2018. https://doi.org/10.1109/ICIEA.2017.828304610.1109/ICIEA.2017.8283046
Search in Google Scholar Back to article
[5] H. Ghazzai, M. B. Ghorbel, A. Kassler, and J. Hossain, “Trajectory optimization for cooperative dual-band UAV swarms,” in Proc. IEEE GLOBECOM, Abu Dhabi, UAE, Dec. 2018, pp. 1–7. https://doi.org/10.1109/GLOCOM.2018.864808710.1109/GLOCOM.2018.8648087
Search in Google Scholar Back to article
[6] M. Chen, F. Dai, H. Wang, and L. Lei, “DFM: A distributed flocking model for UAV swarm networks,” IEEE Access, vol. 6, pp. 69141–69150, 2018. https://doi.org/10.1109/ACCESS.2018.288048510.1109/ACCESS.2018.2880485
Search in Google Scholar Back to article
[7] Y. Liu, J. V. Schijndel, S. Longo, and E. C. Kerrigan, “UAV energy extraction with incomplete atmospheric data using MPC,” IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 2, April 2015, pp. 1203–1215. https://doi.org/10.1109/TAES.2014.13065710.1109/TAES.2014.130657
Search in Google Scholar Back to article
[8] Y. D. Liu, and L. Ziarek, “Toward Energy-Aware Programming for Unmanned Aerial Vehicles,” 2017 IEEE/ACM 3rd International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS), Buenos Aires, 2017, pp. 30–33. https://doi.org/10.1109/SEsCPS.2017.810.1109/SEsCPS.2017.8
Search in Google Scholar Back to article
[9] R.-J. Wai, and A. S. Prasetia, “Adaptive Neural Network Control and Optimal Path Planning of UAV Surveillance System with Energy Consumption Prediction,” in IEEE Access, vol. 7, pp. 126137–126153, 2019. https://doi.org/10.1109/ACCESS.2019.293827310.1109/ACCESS.2019.2938273
Search in Google Scholar Back to article
[10] C. Liu, W. Feng, J. Wang, Y. Chen, and N. Ge, “Aerial Small Cells Using Coordinated Multiple UAVs: An Energy Efficiency Optimization Perspective,” in IEEE Access, vol. 7, pp. 122838–122848, 2019. https://doi.org/10.1109/ACCESS.2019.293825610.1109/ACCESS.2019.2938256
Search in Google Scholar Back to article
[11] F. Morbidi, R. Cano, and D. Lara, “Minimum-energy path generation for a quadrotor UAV,” 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, 2016, pp. 1492–1498. https://doi.org/10.1109/ICRA.2016.748728510.1109/ICRA.2016.7487285
Search in Google Scholar Back to article
[12] S. Spedicato and G. Notarstefano, “Minimum-Time Trajectory Generation for Quadrotors in Constrained Environments,” in IEEE Transactions on Control Systems Technology, vol. 26, no. 4, pp. 1335–1344, July 2018. https://doi.org/10.1109/TCST.2017.270926810.1109/TCST.2017.2709268
Search in Google Scholar Back to article
[13] R. Mahony, V. Kumar and P. Corke, “Multirotor Aerial Vehicles: Modeling, Estimation, and Control of Quadrotor,” in IEEE Robotics & Automation Magazine, vol. 19, no. 3, pp. 20–32, Sept. 2012. https://doi.org/10.1109/MRA.2012.220647410.1109/MRA.2012.2206474
Search in Google Scholar Back to article
[14] P. Apse-Apsitis, A. Avotins, and R. Porins, “Industrial Greenhouse Electrical Power Monitoring Using Secure Internet-of-Things (IoT) Platform,” 2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE 2018), pp 1–4. https://doi.org/10.1109/AIEEE.2018.859230710.1109/AIEEE.2018.8592307
Search in Google Scholar Back to article

Adaptive Traction Drive Control Algorithm for Electrical Energy Consumption Minimisation of Autonomous Unmanned Aerial Vehicle

Abstract

Paradigm

My account