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Trajectory Tracking and Control of Unmanned Aerial Vehicle (UAV): A Particle Swarm Optimization-Based Approach

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Artificial Intelligence and Data Science Based R&D Interventions (NERC 2022)

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Abstract

Quadrotor Unmanned Aerial Vehicle (QUAV) is a member of the Unmanned Aerial Vehicle (UAV) family. The QUAVs have vertical take-off and landing (VTOL) capabilities and high maneuverability. Hence, these are being widely used for applications like logistics, defense, surveillance, etc. Accurate trajectory tracking of the QUAV is an important aspect for several critical applications like commercial package delivery, surveillance, military operations, and infrastructure inspection. However, still, there are plenty of scopes for improvement in trajectory tracking methods. In this work, a PD/PI/PID-based trajectory tracking control strategy is used, while Particle Swarm Optimization (PSO) algorithm is used to tune the PD/PI/PID controller for obtaining the optimal controller gains. The simulation results show that the controller is capable of tracking the desired trajectories (e.g. spiral, circular and helical) efficiently.

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Acknowledgements

The work was supported by ARTPARK (AI & Robotics Technology Park) for the project entitled ‘UAV-UGV Coordination and Formation Control for Unmanned Delivery Services: An Experimental Study’.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, National Institute of Technology Sikkim, Sikkim, India

    Shreyansh Sharma, Abhinav Kumar Singh, Omkar Singh & Anjan Kumar Ray

Authors
  1. Shreyansh Sharma
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  2. Abhinav Kumar Singh
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  3. Omkar Singh
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  4. Anjan Kumar Ray
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Corresponding author

Correspondence to Anjan Kumar Ray .

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Editors and Affiliations

  1. Department of Electronics and Electrical Engineering and Head Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Ratnajit Bhattacharjee

  2. Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, India

    Debanga Raj Neog

  3. Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, India

    Konda Reddy Mopuri

  4. Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Guwahati, India

    Santosh Kumar Vipparthi

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sharma, S., Singh, A.K., Singh, O., Ray, A.K. (2023). Trajectory Tracking and Control of Unmanned Aerial Vehicle (UAV): A Particle Swarm Optimization-Based Approach. In: Bhattacharjee, R., Neog, D.R., Mopuri, K.R., Vipparthi, S.K. (eds) Artificial Intelligence and Data Science Based R&D Interventions. NERC 2022. Springer, Singapore. https://doi.org/10.1007/978-981-99-2609-1_8

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  • DOI: https://doi.org/10.1007/978-981-99-2609-1_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2608-4

  • Online ISBN: 978-981-99-2609-1

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