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This study aims to achieve rapid and stable control of quadrotor unmanned aerial vehicles' (UAVs) attitude by using an Active Disturbance Rejection Control (ADRC) controller. Addressing the challenge of numerous and complex ADRC parameters, optimization algorithms are employed for parameter tuning. This paper draws on the group mechanism of the Ant Colony Optimization (ACO) algorithm and innovatively introduces population search into the Beetle Antennae Search (BAS) algorithm. The refined algorithm is then applied to tune the ADRC parameters, reducing complexity and human intervention while enhancing intelligence and efficiency. The advanced optimization algorithm exhibits an exceptional global optimization capacity, convergence speed, and stability. Ultimately, flight simulation and experimental results suggest that the optimized ADRC controller demonstrates superior control and antidisturbance capabilities. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Attitude Control of UAVs with Search Optimization and Disturbance Rejection Strategies" published in the journal Mathematics. The correction involves the removal of an author, Dr. Chunbo Luo, from the authorship list. Dr. Luo did not consider his contribution sufficient for authorship and requested to be removed. The correction does not impact the scientific conclusions of the paper. The remaining authors are Wensheng Li, Fanke Yang, Liqiang Zhong, Hao Wu, Xiangyuan Jiang, and Andrei V. Chukalin. [Extracted from the article]