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A Review of the Various Control Algorithms for Trajectory Control of Unmanned Underwater Vehicles

Author

Listed:
  • Adeel Bashir

    (Department of Electrical Engineering, COMSATS University, Islamabad 45550, Pakistan
    These authors contributed equally to this work.)

  • Sikandar Khan

    (Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
    These authors contributed equally to this work.)

  • Naveed Iqbal

    (Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
    Center of Energy and Geo Processing, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Salem Bashmal

    (Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
    Interdisciplinary Research Center for Intelligent Manufacturing and Robotics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Sami Ullah

    (K. A. CARE Energy Research & Innovation Center (ERIC), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Fayyaz

    (Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Muhammad Usman

    (Interdisciplinary Research Center for Hydrogen and Energy Storage (IRC-HES), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Unmanned underwater vehicles (UUVs) have become increasingly popular in recent years due to their use in various applications. The motivations for using UUVs include the exploration of difficult and dangerous underwater environments, military tasks in mine detection, intelligence gathering and surveillance, the inspection of offshore oil and gas infrastructure in the oil and gas industry, scientific research for studying marine life, and the search and rescue of missing persons or submerged airplanes or boats in underwater environments. UUVs offer many advantages in achieving the desired applications with increased safety, efficiency, and cost-effectiveness. However, there are also several challenges associated with their communication, navigation, power requirements, maintenance, and payload limitations. These types of vehicles are also prone to various disturbances caused by currents of the ocean, propulsion systems, and unmolded uncertainties. Practically, it is a challenging task to design a controller that will ensure optimal performance under these conditions. Therefore, the control system design is of prime importance in the overall development of UUVs. Also, the UUV controller receives input from different sensors, and the data from these sensors are used by the controller to perform different tasks. The control systems of UUVs should take into account all uncertainties and make them stable so that all sensors can perform optimally. This paper presents a complete review of different control system design algorithms for UUVs. The basic logic designs of several control system algorithms are also presented. A comparison is made based on reliability, robustness, precession, and the ability of the controller to handle the nonlinearity that is faced by UUVs during their missions. Simulation and experimental results are thoroughly studied to gain insight into each algorithm. The advantages and disadvantages of each algorithm are also presented, which will facilitate the selection of a suitable algorithm for the control system design of UUVs.

Suggested Citation

  • Adeel Bashir & Sikandar Khan & Naveed Iqbal & Salem Bashmal & Sami Ullah & Fayyaz & Muhammad Usman, 2023. "A Review of the Various Control Algorithms for Trajectory Control of Unmanned Underwater Vehicles," Sustainability, MDPI, vol. 15(20), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14691-:d:1256848
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    References listed on IDEAS

    as
    1. Pierpaolo Dini & Sergio Saponara, 2020. "Design of Adaptive Controller Exploiting Learning Concepts Applied to a BLDC-Based Drive System," Energies, MDPI, vol. 13(10), pages 1-20, May.
    2. Min Chen & Ashutosh Sharma & Jyoti Bhola & Tien V. T. Nguyen & Chinh V. Truong, 2022. "Multi-agent task planning and resource apportionment in a smart grid," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 444-455, March.
    3. Hongfei Yao & Hongjian Wang & Ying Wang, 2020. "UUV Autonomous Decision-Making Method Based on Dynamic Influence Diagram," Complexity, Hindawi, vol. 2020, pages 1-14, June.
    Full references (including those not matched with items on IDEAS)

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