Abstract
Dynamics and flight stabilization of a multirotor unmanned aerial vehicle (UAV) can be shaped by appropriate mechanisms of tuning parameters of its position and orientation controllers. In the article, the attention is focused on a fixed-parameters altitude controller. Its gains can be tuned optimally and automatically according to the expected criterion, and the search process takes place during the UAV short-time flight. For this purpose, it is proposed to use the auto-tuning method based on the bootstrapping technique and zero-order optimization using Fibonacci-search algorithm. The theoretical basis of the proposed method and discussion of the results from conducted simulation experiments for the exemplary quadrotor model, are presented in the paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bonaccorso, G.: Mastering Machine Learning Algorithms. Expert Techniques to Implement Popular Machine Learning Algorithms and Fine-Tune Your Models. Packt Publishing, Birmingham (2018)
Mohammed, M., Khan, M.B., Bashier, M.B.E.: Machine Learning: Algorithms and Applications. CRC Press, Boca Raton (2016)
Valavanis, K., Vachtsevanos, G.V. (eds.): Handbook of Unmanned Aerial Vehicles. Springer, Heidelberg (2015)
Koch, W., Mancuso, R., West, R., Bestavros, A.: Reinforcement Learning for UAV Attitude Control. arXiv (2018). https://arxiv.org/abs/1804.04154
Ramirez-Atencia, C., Rodriguez-Fernandez, V., Gonzalez-Pardo, A., Camacho, D.: New artificial intelligence approaches for future UAV ground control stations. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 2775–2782. IEEE Press (2017). https://doi.org/10.1109/CEC.2017.7969645
Imanberdiyev, N., Fu, C., Kayacan, E., Chen, I-M.: Autonomous navigation of UAV by using real-time model-based reinforcement learning. In: 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1–6. IEEE Press (2017). https://doi.org/10.1109/ICARCV.2016.7838739
Rodriguez-Ramos, A., Sampedro, C., Bavle, H., de la Puente, P., Campoy, P.: A deep reinforcement learning strategy for UAV autonomous landing on a moving platform. J. Intell. Robot. Syst. 1–16 (2018). https://doi.org/10.1007/s10846-018-0891-8
Berkenkamp, F., Schoellig, A.P., Krause, A.: Safe controller optimization for quadrotors with Gaussian processes. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 491–496. IEEE Press, Sweden (2016). https://doi.org/10.1109/ICRA.2016.7487170
Muliadi, J., Kusumoputro, B.: Neural network control system of UAV altitude dynamics and its comparison with the PID control system. J. Adv. Transp. 1–18 (2018). https://doi.org/10.1155/2018/3823201
Carrio, A., Sampedro, C., Rodriguez-Ramos, A., Campoy, P.: A review of deep learning methods and applications for unmanned aerial vehicles. J. Sens. 1–13 (2017). https://doi.org/10.1155/2017/3296874
Giernacki, W., Fraire, T.E., Kozierski, P.: Cuttlefish optimization algorithm in autotuning of altitude controller of unmanned aerial vehicle (UAV). In: Third Iberian Robotics Conference. ROBOT 2017. AISC, vol. 693, pp. 841–852. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-70833-1_68
Alikhani, H.: PID type iterative learning control with optimal variable coefficients. In: 2010 5th IEEE International Conference Intelligent Systems, pp. 479–484. IEEE Press, London (2010). https://doi.org/10.1109/IS.2010.5548329
Berkenkamp, F., Krause, A., Schoellig, A.: Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics. arXiv (2018). https://arxiv.org/pdf/1602.04450.pdf
Corke, P.: Robotics, Vision and Control: Fundamental Algorithms in MATLAB\(^{\textregistered }\) Second, Completely Revised, Extended and Updated Edition (Springer Tracts in Advanced Robotics). Springer, Heidelberg (2017)
Giernacki, W., Horla, D., Espinoza Fraire, T.: Strategy for Optimal Autotuning of the Fixed-Wing UAV Controllers by the Use of Zero-order Optimization Algorithm (in review)
Horla, D.: Computational Methods in Optimization, 2nd edn, p. 358. Publishing House of Poznan University of Technology, Poznan (2016). (in Polish)
Kiefer, J.: Sequential minimax search for a maximum. Proc. Am. Math. Soc. 4(3), 502–506 (1953). https://doi.org/10.2307/2032161
Lewandowski, M.: Optimization methods – theory and selected algorithms. https://web.sgh.waw.pl/~mlewan1/Site/MO_files/mo_skrypt_21_12.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Giernacki, W. (2020). Optimal Tuning of Altitude Controller Parameters of Unmanned Aerial Vehicle Using Iterative Learning Approach. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2019. AUTOMATION 2019. Advances in Intelligent Systems and Computing, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-030-13273-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-13273-6_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-13272-9
Online ISBN: 978-3-030-13273-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)