Attitude Control of UAVs with Search Optimization and Disturbance Rejection Strategies

Abstract

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.