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<p>This study proposes an energy-efficient control strategy for the soft landing of quadrotors on a fixed platform under external disturbances, such as wind gusts. The primary objective is to minimize energy consumption in quadrotor motors, enabling the use of smaller batteries and lighter systems. A hybrid Grey Wolf Optimizer-Sliding Mode Control (GWOSMC) algorithm is developed, leveraging the Grey Wolf Optimization (GWO) algorithm to tune control parameters dynamically. This approach is compared with Fuzzy-Sliding Mode Control (FSMC) and classical Sliding Mode Control (SMC) through MATLAB simulations. The results show that the GWOSMC method not only maintains strong tracking accuracy but also significantly reduces energy consumption compared to the FSMC and SMC methods up to 2.5 and 22 times, respectively. The findings highlight GWOSMC's potential for enhancing quadrotor autonomy and efficiency in real-world applications, particularly in spatial information systems requiring precise navigation and energy management. The study underscores the importance of metaheuristic optimization in advancing UAV control strategies.</p>

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Improvement of the consumption energy of quadrotor motors for soft landings on a platform using the Grey Wolf Optimization algorithm in the presence of external disturbance

  • 期刊论文

  • 发表日期：

    2025年7月5日

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  • Farsimadan Anna,
  • Mazinan Amir Hooshang,
  • Siahi Mehdi

摘要

This study proposes an energy-efficient control strategy for the soft landing of quadrotors on a fixed platform under external disturbances, such as wind gusts. The primary objective is to minimize energy consumption in quadrotor motors, enabling the use of smaller batteries and lighter systems. A hybrid Grey Wolf Optimizer-Sliding Mode Control (GWOSMC) algorithm is developed, leveraging the Grey Wolf Optimization (GWO) algorithm to tune control parameters dynamically. This approach is compared with Fuzzy-Sliding Mode Control (FSMC) and classical Sliding Mode Control (SMC) through MATLAB simulations. The results show that the GWOSMC method not only maintains strong tracking accuracy but also significantly reduces energy consumption compared to the FSMC and SMC methods up to 2.5 and 22 times, respectively. The findings highlight GWOSMC's potential for enhancing quadrotor autonomy and efficiency in real-world applications, particularly in spatial information systems requiring precise navigation and energy management. The study underscores the importance of metaheuristic optimization in advancing UAV control strategies.