Abstract
Unmanned Aerial Vehicles (UAVs) have been extensively used in civil and industrial applications due to the rapid development of the involved technologies. Especially, using deep reinforcement learning methods for motion control acquires a major progress recently since deep Q-learning has successfully applied to the continuous action domain problem. This paper proposes a new Deep Deterministic Policy Gradient (DDPG) algorithm for path following control problem of UAV with sensor faults. Firstly, the model of UAV path following problem has been established. After that, the DDPG framework is constructed. Then, the proposed DDPG algorithm is formulated to the path following problem. Finally, simulation results are carried out to show the efficiency and effectiveness of the proposed methodology.
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
References
Liu, Z.X., Yuan, C., Zhang, Y.M.: Active fault-tolerant control of unmanned quadrotor helicopter using linear parameter varying technique. J. Intell. Robot. Syst. (2017)
Qu, Y.H., Zhang, Y.T., Zhang, Y.M.: Optimal flight path planning for UAVs in 3-D threat environment. In: International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, USA (2014)
Liu, Z.X., Zhang, Y.M., Yu, X., Yuan, C.: Unmanned surface vehicles: an overview of developments and challenges. Annu. Rev. Control (2016)
Qu, Y.H., Zhang, Y.T., Zhang, Y.M.: A UAV solution of regional surveillance based on pheromones and artificial potential field theory. In: International Conference on Unmanned Aircraft Systems (ICUAS), Denver, USA (2015)
Li, P., Yu, X., Peng, X.Y., Zheng, Z.Q., Zhang, Y.M.: Fault-tolerant cooperative control for multiple UAVs based on sliding mode techniques. Sci. China Inf. Sci. (2017)
Zhang, Y.T., Zhang, Y.M., Liu, Z.X., Yu, Z.Q., Qu, Y.H.: Line-of-sight path following control on UAV with sideslip estimation and compensation. In: 37th Chinese Control Conference (CCC), Wuhan, China (2018)
Fossen, T.I., Breivik, M., Skjetne, R.: Line-of-sight path following of underactuated marine craft. In: 6th IFAC Conference on Manoeuvring and Control of Marine Craft, Girona, Spain (2003)
Breivik, M.: Topics in guided motion control of marine vehicles. In: Tapir Uttrykk (2010)
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. (2019)
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G, Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. Nature (2015)
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., Riedmiller, M.: Deterministic policy gradient algorithms (2014)
Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D.: Continuous control with deep reinforcement learning. ArXiv preprint (2015)
Rysdyk, R.: Unmanned aerial vehicle path following for target observation in wind. J. Guid. Control Dyn. (2006)
Qie, H., Shi, D.X., Shen, T.L., Xu, X.H., Li, Y., Wang, L.J.: Joint optimization of multi-UAV target assignment and path planning based on multi-agent reinforcement learning. IEEE Access (2019)
Wawrzynski, P.: Control policy with autocorrelated noise in reinforcement learning for robotics. Int. J. Mach. Learn. Comput. (2015)
Acknowledgements
This work is supported by National Natural Science Foundation of China (No. 61833013 and 61573282) and Natural Sciences and Engineering Research Council of Canada.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, Y., Zhang, Y., Yu, Z. (2022). A Deep Reinforcement Learning Strategy for UAV Path Following Control Under Sensor Fault. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_432
Download citation
DOI: https://doi.org/10.1007/978-981-15-8155-7_432
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8154-0
Online ISBN: 978-981-15-8155-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)